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        "<a href=\"https://colab.research.google.com/github/034adarsh/Stock-Price-Prediction-Using-LSTM/blob/main/LSTM_Improved_model(diff_dataset).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "# Import all the required libraries\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "yrU0iWdalVGx"
      },
      "id": "yrU0iWdalVGx"
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      "execution_count": 1,
      "id": "4189cf86",
      "metadata": {
        "scrolled": false,
        "id": "4189cf86"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import datetime as dt\n",
        "from datetime import date\n",
        "import matplotlib.pyplot as plt\n",
        "import yfinance as yf\n",
        "import numpy as np\n",
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Define start day to fetch the dataset from the yahoo finance library\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "Nij98iZElfyQ"
      },
      "id": "Nij98iZElfyQ"
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    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "d20a4c49",
      "metadata": {
        "scrolled": false,
        "id": "d20a4c49"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "START = \"2010-01-01\"\n",
        "TODAY = date.today().strftime(\"%Y-%m-%d\")\n",
        "\n",
        "# Define a function to load the dataset\n",
        "\n",
        "def load_data(ticker):\n",
        "    data = yf.download(ticker, START, TODAY)\n",
        "    data.reset_index(inplace=True)\n",
        "    return data"
      ]
    },
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      "execution_count": 3,
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          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
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          "data": {
            "text/plain": [
              "        Date        Open        High         Low       Close   Adj Close  \\\n",
              "0 2010-01-04  377.399994  379.450012  374.524994  375.825012  289.802155   \n",
              "1 2010-01-05  377.500000  379.774994  373.274994  375.924988  289.879333   \n",
              "2 2010-01-06  375.924988  376.100006  366.500000  367.424988  283.325012   \n",
              "3 2010-01-07  367.750000  369.700012  355.500000  357.200012  275.440186   \n",
              "4 2010-01-08  358.000000  359.250000  348.250000  349.899994  269.811127   \n",
              "\n",
              "    Volume  \n",
              "0  1963682  \n",
              "1  2014488  \n",
              "2  3349176  \n",
              "3  6474892  \n",
              "4  6048178  "
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-01-05</td>\n",
              "      <td>377.500000</td>\n",
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              "      <th>2</th>\n",
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              "      <th>3</th>\n",
              "      <td>2010-01-07</td>\n",
              "      <td>367.750000</td>\n",
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              "      <td>275.440186</td>\n",
              "      <td>6474892</td>\n",
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              "      <th>4</th>\n",
              "      <td>2010-01-08</td>\n",
              "      <td>358.000000</td>\n",
              "      <td>359.250000</td>\n",
              "      <td>348.250000</td>\n",
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      ],
      "source": [
        "data = load_data('TCS.NS')\n",
        "df=data\n",
        "df.head()"
      ]
    },
    {
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              "         Open        High         Low       Close   Volume\n",
              "0  377.399994  379.450012  374.524994  375.825012  1963682\n",
              "1  377.500000  379.774994  373.274994  375.924988  2014488\n",
              "2  375.924988  376.100006  366.500000  367.424988  3349176\n",
              "3  367.750000  369.700012  355.500000  357.200012  6474892\n",
              "4  358.000000  359.250000  348.250000  349.899994  6048178"
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          "metadata": {},
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      ],
      "source": [
        "df = df.drop(['Date', 'Adj Close'], axis = 1)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizing Closing Price"
      ],
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        "id": "M-ojrPeXOxPq"
      },
      "id": "M-ojrPeXOxPq"
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(df['Close'])\n",
        "plt.title(\"TCS India Stock Price\")\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"Price (INR)\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
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          "height": 564
        },
        "id": "DCvdjb9gOhar",
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      "id": "DCvdjb9gOhar",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "73888072",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "73888072",
        "outputId": "c3ff127c-8c35-4bb6-a18d-a7880266b57f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Open         High          Low        Close   Volume\n",
              "0      377.399994   379.450012   374.524994   375.825012  1963682\n",
              "1      377.500000   379.774994   373.274994   375.924988  2014488\n",
              "2      375.924988   376.100006   366.500000   367.424988  3349176\n",
              "3      367.750000   369.700012   355.500000   357.200012  6474892\n",
              "4      358.000000   359.250000   348.250000   349.899994  6048178\n",
              "...           ...          ...          ...          ...      ...\n",
              "3288  3235.000000  3235.000000  3210.000000  3219.399902  2321159\n",
              "3289  3216.000000  3216.000000  3173.149902  3179.899902  2468285\n",
              "3290  3180.149902  3224.449951  3178.000000  3220.699951  1953253\n",
              "3291  3220.000000  3244.000000  3210.050049  3230.699951  1710027\n",
              "3292  3245.000000  3254.850098  3231.300049  3250.600098   928235\n",
              "\n",
              "[3293 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-391f8155-e1fc-42e7-9713-d56c80d6fe8b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>377.399994</td>\n",
              "      <td>379.450012</td>\n",
              "      <td>374.524994</td>\n",
              "      <td>375.825012</td>\n",
              "      <td>1963682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>377.500000</td>\n",
              "      <td>379.774994</td>\n",
              "      <td>373.274994</td>\n",
              "      <td>375.924988</td>\n",
              "      <td>2014488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>375.924988</td>\n",
              "      <td>376.100006</td>\n",
              "      <td>366.500000</td>\n",
              "      <td>367.424988</td>\n",
              "      <td>3349176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>367.750000</td>\n",
              "      <td>369.700012</td>\n",
              "      <td>355.500000</td>\n",
              "      <td>357.200012</td>\n",
              "      <td>6474892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>358.000000</td>\n",
              "      <td>359.250000</td>\n",
              "      <td>348.250000</td>\n",
              "      <td>349.899994</td>\n",
              "      <td>6048178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3288</th>\n",
              "      <td>3235.000000</td>\n",
              "      <td>3235.000000</td>\n",
              "      <td>3210.000000</td>\n",
              "      <td>3219.399902</td>\n",
              "      <td>2321159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3289</th>\n",
              "      <td>3216.000000</td>\n",
              "      <td>3216.000000</td>\n",
              "      <td>3173.149902</td>\n",
              "      <td>3179.899902</td>\n",
              "      <td>2468285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3290</th>\n",
              "      <td>3180.149902</td>\n",
              "      <td>3224.449951</td>\n",
              "      <td>3178.000000</td>\n",
              "      <td>3220.699951</td>\n",
              "      <td>1953253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3291</th>\n",
              "      <td>3220.000000</td>\n",
              "      <td>3244.000000</td>\n",
              "      <td>3210.050049</td>\n",
              "      <td>3230.699951</td>\n",
              "      <td>1710027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3292</th>\n",
              "      <td>3245.000000</td>\n",
              "      <td>3254.850098</td>\n",
              "      <td>3231.300049</td>\n",
              "      <td>3250.600098</td>\n",
              "      <td>928235</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3293 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-391f8155-e1fc-42e7-9713-d56c80d6fe8b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-391f8155-e1fc-42e7-9713-d56c80d6fe8b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-391f8155-e1fc-42e7-9713-d56c80d6fe8b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Plotting moving averages of 100 day\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "UMkTEOfBlzte"
      },
      "id": "UMkTEOfBlzte"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "114d2e0c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "114d2e0c",
        "outputId": "aad49169-4922-4f48-bd62-84ec26fa3768"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0               NaN\n",
              "1               NaN\n",
              "2               NaN\n",
              "3               NaN\n",
              "4               NaN\n",
              "           ...     \n",
              "3288    3305.993005\n",
              "3289    3303.533005\n",
              "3290    3301.886504\n",
              "3291    3300.387502\n",
              "3292    3299.388003\n",
              "Name: Close, Length: 3293, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "ma100 = df.Close.rolling(100).mean()\n",
        "ma100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "e2f6b740",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "e2f6b740",
        "outputId": "136dd8a6-23d1-4fef-d676-8db5fed9e4b5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Graph Of Moving Averages Of 100 Days')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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vaDmib7/9FlFRUVi+fLnDc9VqNapWrWq3/8qVKxg+fDjKly8PLy8vVKxYUfoHr+38WKVSiVq1asn21alTB4C4LJi16tWry55bAkTrOcWOeHt7y/4B74irAZArnnnmGaxZswbnz5/Hvn37nA5Lt8yTtq5+DYhDf2vVqiWbRz1o0CCkp6fj999/ByDer82bN2PAgAEO5zDbyu2zs7yWbcVttVrt0vx4QAymDQYDmjZtivPnz+P8+fO4e/cuWrVqJRuePmDAACiVSmm4sCAIWLNmDbp37y4NbT537hwAcQk56x+DKlasiEWLFkGv19t9l4KDg+36dPfuXbz88ssIDAyEu7s7KlasKLWznJ+QkID09HSH1cZt9507dw6CIKB27dp2/fr333+RkJAg9WXy5MlYtGgRKlSogMjISMyfPz/X+eGW758r39eC+K7mxBLo6vV6u2MZGRmyQNjd3d1pO+tr5Zflv0/W7/nUqVN46qmn4OvrCx8fH1SsWBHPPvssAPl/ZwYNGgSdTid9BxMTE7Fx40ZpSUEAaN++Pfr164cZM2agQoUK6N27NxYvXuzwPRERlXRcvoyIqBjw9fVFpUqVcPLkSbtjljnjtgGxhU6nk2VyATG72LVrV9y9exfTpk1DvXr14OnpievXr2P48OEPtB6ws3nQgpPCbhb169fH0aNHodfrHWbtALGol0ajQe3atfPdP4unn34a06dPx+jRo+Hv74+IiIgHvmbr1q1Rs2ZNrF69Gs888ww2bNiA9PR0DBo0yKXz8/vZ5YUl0GnTpo3D4xcvXkStWrVQuXJlPP7441i9ejVef/117N+/H1euXMHHH38stbV8Tz799FM0adLE4fVsR3E4CvYGDhyIffv2YcqUKWjSpAm8vLxgNpvRrVu3fH0XzWaztD66o8/Uuk+zZ8/G8OHDsX79emzbtg0vvfQSZs2ahf379zv8AQsQv6uA+H105vLly0hKSkJYWFie+58XlsKNN2/etDt28+ZN2eiBSpUq4fr16w7bAZC1zQ/Lf58sP4zcv38f7du3h4+PD9577z2EhITAzc0NR44cwbRp02T3tly5cujVqxdWrFiBt99+G2vXroVer5eCdkAcdbJ27Vrs378fGzZswNatWzFy5EjMnj0b+/fvdzhiiIiopGIgTkRUTPTs2ROLFi3CgQMHpOJK+XXixAn8999/WLp0qaw4m3WRJWtmsxkXL16UsuAApMrIrmZic9OrVy9ER0djzZo1sn98W8TGxuKvv/5Cly5dHjhzB4jZ5zZt2mDXrl0YO3asbKi3NUtl9rNnz8pGBWRmZuLSpUvo0qWLrP3AgQMxd+5cJCUl4eeff0bNmjWlYdYPytKX8+fPo2PHjtJ+o9GI2NhYNGrUKMfzL126hH379mH8+PHS6AcLs9mMoUOHYuXKldIa64MGDcL//vc/nD17Fj///DM8PDzwxBNPSOdYqtv7+PjYfQ6uunfvHnbs2IEZM2bg7bfflvZbsu0WAQEBcHNzc1hF3nZfSEgIBEFAcHCw7DvrTMOGDdGwYUO8+eab2LdvH9q0aYNvvvkGH3zwgcP2derUQZ06dfDbb79h7ty5DrPeP/74IwDxe12YGjRoALVajUOHDskKHWZmZiImJka2r0mTJti5cyeSkpJkBdv++ecf6fiDWLZsGRQKBbp27QpArHB/584d/Prrr2jXrp3U7tKlSw7PHzZsGHr37o2DBw9ixYoVaNq0KR555BG7dq1bt0br1q0xc+ZMrFy5EkOGDMFPP/2EUaNGPVD/iYiKEw5NJyIqJqZOnQoPDw+MHDkS8fHxdsfzkjW1ZAmtzxEEAXPnznV6zrx582Rt582bB41Gg86dO7v8ujl58cUXERAQgClTptjNJ8/IyMCIESMgCIIsWHtQH3zwAd555x1MmDDBaZsuXbpAq9Xiyy+/lH1e33//PRITE9GzZ09Z+0GDBkGv12Pp0qXYsmWL0yrw+dGiRQv4+/tj4cKFMBqN0v4VK1bkOvTf0g4Qv0v9+/eX/Rk4cCDat28vG57er18/qFQqrFq1CmvWrEGvXr3g6ekpHW/evDlCQkLw2WefOZw24Wg5KluOvosA7Crgq1QqdOnSBb/99htu3Lgh7T9//jz++OMPWdu+fftCpVJhxowZdtcVBEFaFi0pKUn2OQJiUK5UKnMd7vz222/j3r17GDNmjGwpLgA4fPgwPv74YzRo0EBapaCw+Pr6okuXLli+fLlsqPyyZcuQkpKCAQMGSPv69+8Pk8mE7777Ttqn1+uxePFitGrVSrb0YV599NFH2LZtGwYNGiSNWHF0bzMzM/H11187vEb37t1RoUIFfPzxx9i9e7fdD3L37t2zu5+WHw84PJ2IShtmxImIionatWtj5cqVePrpp1G3bl0MGTIEjRs3hiAIuHTpElauXAmlUul0OK21evXqISQkBK+++iquX78OHx8f/PLLL06DOTc3N2zZsgXPPfccWrVqhT/++AObNm3C66+/jooVKxbI+/P398fatWvRs2dPNGvWDKNGjUJYWBji4uKwZMkSnD9/HnPnzsVjjz1WIK8HiHNObTPDtipWrIjp06djxowZ6NatG5588kmcPXsWX3/9NVq2bGkXLDRr1gyhoaF44403oNfrXR6W7gqtVot3330XEyZMQKdOnTBw4EDExsZiyZIlCAkJyXUe+ooVK9CkSROnAdeTTz6JCRMm4MiRI2jWrBkCAgLQsWNHfP7550hOTrZ7L0qlEosWLUL37t3xyCOPYMSIEahSpQquX7+OnTt3wsfHBxs2bMixTz4+PmjXrh0++eQTGAwGVKlSBdu2bXOYNX333Xexbds2tGnTBmPHjoXJZMK8efPQoEED2dJbISEh+OCDDzB9+nTExsaiT58+8Pb2xqVLl7Bu3Tq88MILePXVV/Hnn39i/PjxGDBgAOrUqQOj0Yhly5ZBpVLlGkAPGTIEBw8exNy5c3H69GmpYN6RI0fwww8/SN9njUaT43VyYsnInzp1CoAYXO/duxcApFELgLhU22OPPYb27dvjhRdewLVr1zB79mxERESgW7duUrtWrVphwIABmD59OhISEhAaGoqlS5ciNjYW33//vUt9MhqNUj2KjIwMXL58Gb///juOHz+Ojh07yoL8xx57DOXKlcNzzz2Hl156CQqFAsuWLXP6o6FGo8HgwYMxb948qFQqWUFBAFi6dCm+/vprPPXUUwgJCUFycjIWLlwIHx8f9OjRw6X+ExGVGEVcpZ2IiHJx/vx5YezYsUJoaKjg5uYmuLu7C/Xq1RPGjBkjxMTEyNrmtNzQ6dOnhS5dugheXl5ChQoVhNGjRwvHjh0TAAiLFy+2u8aFCxeEiIgIwcPDQwgMDBTeeecd2XJeOS0lBkB45513XHp/ly5dEkaPHi1Ur15d0Gg0QoUKFYQnn3xSWmrMtq2z18xvW2ef2bx584R69eoJGo1GCAwMFMaOHSvcu3fP4TXeeOMNAYAQGhrq8Liz5ctsl9Gy9Nn6fgiCIHz55ZdCjRo1BJ1OJzz66KPC33//LTRv3lzo1q2b0/d1+PBhAYDw1ltvOW0TGxtrt/zUwoULBQCCt7e3kJ6e7vC8o0ePCn379hX8/f0FnU4n1KhRQxg4cKCwY8cOqU1OS3ddu3ZNeOqppwQ/Pz/B19dXGDBggHDjxg2H35sdO3YITZs2FbRarRASEiIsWrRIeOWVVwQ3Nze76/7yyy9C27ZtBU9PT8HT01OoV6+eMG7cOOHs2bOCIAjCxYsXhZEjRwohISGCm5ubUL58eaFjx47C9u3bnX5Gtn777Teha9euQrly5QSdTieEhoYKr7zyisP3mdflywA4/WPrr7/+Eh577DHBzc1NqFixojBu3DghKSnJrl16errw6quvCkFBQYJOpxNatmwpbNmyxaX+WJYytPzx8PAQatasKfTr109Yu3at7L8HFn///bfQunVrwd3dXahcubIwdepUYevWrU6XY7MskRcREWF37MiRI8LTTz8tVK9eXdDpdEJAQIDQq1cv4dChQy71n4ioJFEIQgFWiCEiohJn+PDhWLt2rcOhx1Q8mM1mVKxYEX379sXChQsfdneKXJ8+fXDq1Cm7eeVU8hw7dgxNmjTBjz/+iKFDhz7s7hARPTScI05ERFSMZGRk2A3t/fHHH3H37l106NDh4XSqCNmudX3u3Dls3ry5TLz3smDhwoXw8vJC3759H3ZXiIgeKs4RJyIiKkb279+PSZMmYcCAAfD398eRI0fw/fffo0GDBrLCXKVVrVq1MHz4cGkN9wULFkCr1WLq1KkPu2v0ADZs2IDTp0/ju+++w/jx42VFAYmIyiIG4kRERMVIzZo1Ua1aNXz55Ze4e/cuypcvj2HDhuGjjz6CVqt92N0rdN26dcOqVasQFxcHnU6H8PBwfPjhhwWytjw9PBMmTEB8fDx69OiBGTNmPOzuEBE9dJwjTkRERERERFSEOEeciIiIiIiIqAgxECciIiIiIiIqQqV2jrjZbMaNGzfg7e0NhULxsLtDREREREREpZwgCEhOTkblypWhVDrPe5faQPzGjRuoVq3aw+4GERERERERlTFXr15F1apVnR4vtYG4t7c3APED8PHxeci9cc5gMGDbtm2IiIiARqN52N2hh4DfgbKN979s4/0nfgfKNt5/4neg9ElKSkK1atWkeNSZUhuIW4aj+/j4FPtA3MPDAz4+PvzLV0bxO1C28f6Xbbz/xO9A2cb7T/wOlF65TY9msTYiIiIiIiKiIsRAnIiIiIiIiKgIMRAnIiIiIiIiKkIMxImIiIiIiIiKEANxIiIiIiIioiLEQJyIiIiIiIioCDEQJyIiIiIiIipCDMSJiIiIiIiIihADcSIiIiIiIqIixECciIiIiIiIqAgxECciIiIiIiIqQgzEiYiIiIiIiIoQA3EiIiIiIiKiIsRAnIiIiIiIiKgIMRAnIiIiIiIiKkIMxImIiIiIiIiK0AMF4h999BEUCgUmTpwo7cvIyMC4cePg7+8PLy8v9OvXD/Hx8bLzrly5gp49e8LDwwMBAQGYMmUKjEajrM2uXbvQrFkz6HQ6hIaGYsmSJQ/SVSIiIiIiIqJiId+B+MGDB/Htt9+iUaNGsv2TJk3Chg0bsGbNGuzevRs3btxA3759peMmkwk9e/ZEZmYm9u3bh6VLl2LJkiV4++23pTaXLl1Cz5490bFjR8TExGDixIkYNWoUtm7dmt/uEhERERERFamV/1zB6+tOwGwWHnZXqJjJVyCekpKCIUOGYOHChShXrpy0PzExEd9//z0+//xzdOrUCc2bN8fixYuxb98+7N+/HwCwbds2nD59GsuXL0eTJk3QvXt3vP/++5g/fz4yMzMBAN988w2Cg4Mxe/Zs1K9fH+PHj0f//v0xZ86cAnjLREREREREhe/1dSew8p8r+PNMwsPuChUz6vycNG7cOPTs2RNdunTBBx98IO0/fPgwDAYDunTpIu2rV68eqlevjujoaLRu3RrR0dFo2LAhAgMDpTaRkZEYO3YsTp06haZNmyI6Olp2DUsb6yHwtvR6PfR6vfQ8KSkJAGAwGGAwGPLzNouEpW/FuY9UuPgdKNt4/8s23n/id6Bs4/0vO67fS3V4n/kdKH1cvZd5DsR/+uknHDlyBAcPHrQ7FhcXB61WCz8/P9n+wMBAxMXFSW2sg3DLccuxnNokJSUhPT0d7u7udq89a9YszJgxw27/tm3b4OHh4fobfEiioqIedhfoIeN3oGzj/S/beP+J34Gyjfe/dBIEwBJuHT1+En63Tzhty+9A6ZGWluZSuzwF4levXsXLL7+MqKgouLm55atjhWX69OmYPHmy9DwpKQnVqlVDREQEfHx8HmLPcmYwGBAVFYWuXbtCo9E87O7QQ8DvQNnG+1+28f4TvwNlG+9/6aY3mID9OwAANUProkeHWnZt+B0ofSwjs3OTp0D88OHDSEhIQLNmzaR9JpMJe/bswbx587B161ZkZmbi/v37sqx4fHw8goKCAABBQUE4cOCA7LqWqurWbWwrrcfHx8PHx8dhNhwAdDoddDqd3X6NRlMivtQlpZ9UePgdKNt4/8s23n/id6Bs4/0vndKsRihr1Koc7zG/A6WHq/cxT8XaOnfujBMnTiAmJkb606JFCwwZMkTa1mg02LFjh3TO2bNnceXKFYSHhwMAwsPDceLECSQkZBcsiIqKgo+PD8LCwqQ21tewtLFcg4iIiIiIqDjLMJqkbROrppONPGXEvb290aBBA9k+T09P+Pv7S/uff/55TJ48GeXLl4ePjw8mTJiA8PBwtG7dGgAQERGBsLAwDB06FJ988gni4uLw5ptvYty4cVJGe8yYMZg3bx6mTp2KkSNH4s8//8Tq1auxadOmgnjPREREREREkvtpmTh9IwnhIf5QKBQFcs30zOxAPNNoLpBrUumR73XEnZkzZw569eqFfv36oV27dggKCsKvv/4qHVepVNi4cSNUKhXCw8Px7LPPYtiwYXjvvfekNsHBwdi0aROioqLQuHFjzJ49G4sWLUJkZGRBd5eIiIiIiMq4PvP/xjOL/sFvMdcL7JrWGfFMEwNxksvX8mXWdu3aJXvu5uaG+fPnY/78+U7PqVGjBjZv3pzjdTt06ICjR48+aPeIiIiIiIhyFHtHrHS98dhNPNW0KgAgPikDFb10UCrzlyFnRpxyUuAZcSIiIiIiopJs59kEtPpwByb+HJPva2QYsoNvPQNxssFAnIiIiIiICIClpNpXO84BAH4/diPf18owMCNOzjEQJyIiIiIispJmNaw8v6asPS5tc4442WIgTkREREREBEAQxJx4QQTit1P00nam8cGvR6ULA3EiIiIiIiIrDxqIWw9LBzg0newxECciIiIiojLLYDVsXJVVIT090/hA19x5JkH2nEPTyRYDcSIiIiIiKrOss99KhRiIG0yCs+a5Onk9EWNXHJHtY0acbDEQJyIiIiKiMst6ve9tp+MxYdXRB8pgn7ieaLePgTjZYiBORERERERlVrrNfO4ND7BkGQD4uGns9umNZmQazTCZ859pp9KFgTgREREREZVJRpMZMzedLtBrqlUKu33pBhM6f74LPb/8S6rMTmWb+mF3gIiIiIiI6GH4fu8lbP83IfeGeeBoGPrlO2nSdrrBBA8tw7CyjhlxIiIiIiIqk349cr3Ar6nPZT74/TRDgb8mlTwMxImIiIiIqEy6fDe1wK9pnRFX2o9SZyBOABiIExERERFRGeWuURX4NfXG7OJvVcq52x3/9cg1vPv7KaToHa9VLggCoi/cwe0UfYH3jYoPTk4gIiIiIqIyyUOrxr0CzlBbD03399QhLjFDti75or2XAAC3U/SYM6Ch3fl/nknA80sPwc9Dg5i3Iwq0b1R8MCNORERERERlknWF80eDy+P5tsGy4+U87Jciy43ekB2Itw2tgAZVfB22++fSXYf7o07HA+AQ9tKOgTgREREREZVJKRnZw8NNZgE6tTw88vPQ5vma1kPTx3cKdTr8/VayHmYH64obudZ4mcBAnIiIiIiIypzzCcm4k5op22ebpTbnY81vy9D0Me1D4KZRwS2HeegJDuaBG005V12n0oFzxImIiIiIqMz5bs9F2XMfN7Xd+t75CcQtVdMt2XU3jfPcp9HEjHhZxUCciIiIiIjKHAXka4sNbFENjar5YdOJm9I+cz6S05ah6bqsANxd4zzkcrTmuIEZ8TKBgTgREREREZU5Cqs4/ON+DdGtQRAUCnlw/iBD07UqMRCvXt7DadtMB4H4reTs4eqCINj1iUoHBuJERERERFTmWAe4bUIrOAx48xWIZ1VN12XNDa9V0dN5W6vCbtfvp2PE4gP4Lz5F2mcwCdCqGYiXRizWRkREREREZY513K1ROQ6L8jNKXBqanjVHXKNyHkhbD03/6I8zsiAcADKsAnUqXRiIExERERFRmaZWOg6WhXxkxC3BtI+bJuvazkMu66HpSen264ZnGBiIl1YMxImIiIiIqMw5cS1R2rYOlptV95O28zo0XW804fr9dABAi5rlAAAqq4y4v6d8XXLrQNzRa1mGuVPpw0CciIiIiIjKnBPXswNx6wLqS0Y+ijd71gcAmPK4lFhKhlHaLuchBt0aqyBfq5aHX9ZD0x3F/HoOTS+1GIgTEREREVGZ5q3LrmHt46ZB5/qBABwHxznZdfaWtK3KGu6utsqI285FN1gF+qmZRtjKYEa81GIgTkREREREZZZOrYTSZo64KquSW16Hpr+y5pjdPutibWqbwm0ms/M54iqzCSc+mgc8+igQFgZMnAjExuapP1R8cfkyIiIiIiIqsxaPaGm3z1JR3ZSPYm0ySUmo8c40bN20DTGV6qLcYy3whjoMt7zE+ePWQ99T9eIw9PnPNMPXn6/GJ398iUcSLmZf699/gSVLgB9/BJ588sH6RQ8dA3EiIiIiIipTrAPgekE+dsctmWujSYAgCA7XGHck0EeH+CQ95g5uIu4YMwYVVq1CBQB1b18BTkShg285rG/ZAz9WbgGDqb50bmJWRrzB3cv4ZdVrcMvMQIrWHabxE+Ab3hL47DPgn3+A3r2Bd94B3n03X++digcOTSciIiIiojIlzWo+todWZXfcsvSY0Swg3cUlxExmAbeS9QCA8Fr+wK+/AqtWQVCp8EPzJ7G5zmMwBNeCNvEeBmxfgQ0/TkLdn34AABy7loh0gwlVE+NR5em+cMvMwD/VGqD9CwtxedJ0oH9/YM8ecXg6AMyYAezalf8PgB46BuJERERERFSmWJYY89apoVPbh0QeWpW0tniig/W9HbmToodZAJQKwN+UAYwfDwC4N2ES3uvyAv731OtIPHwMWL0ap5u0AQC0mv8hAg4fxpu/nYJHZjoWr3kX6ribQFgYZo+ZhTuefribmim+gFYLzJkDjB0rPp8wATCzmFtJxUCciIiIiIjKlIu3UgEAoYFeDoedKxQKGLOGr/904KpL14xLygAAVPTWQfX+e8DNm0Dt2rg5/lWpjZeXOzBgABa99hVWNOkGhSCgxezZaPH3Zixd/Q5q37kKc6XKwLZtgL8/ACBFb1NN/YMPAD8/4ORJYMOGvL51KiYYiBMRERERUZmSnCFmuS1rfedk7o5zEJwUbftyxzksi44FAMQnicPSQ1WZwLffZjX4Eh6+XlJ7S/ZdrVJiRucXkRAaBk1aGj745RO0vH4aSVoPZP70E1ClilRt3W4t8/Lls7PiM2fmfY01KhYYiBMRERERUZky7ZcTAICMHOZ/d6xbUdq2DGW3dv1+Oj6P+g9vrT+Fmq9twm8x1wEAAw5sANLSgEaNgMhIBFfwxBs96mPOoMZS9l2tUiJTrcHG1z/HkeZtcTIwBJvrPIanhs6Guo04bF2lFEM1vdHB8POXXwY8PYGDB4Hff8/fh0APFaumExERERFRmbTvwh2nx9ytirhdup2KquU8ZMctWXWLTcdvwjc9GZFbVog7XntNWgdtdLtasraW+ed3KlVH3y6vyY+pxABck9Vm6trjaFzVD3WDvLMbBQYCL70EzJolDlV/4glAyRxrScK7RUREREREZMN6SPjtFL3d8fRM+2z6mH9+gXtaspgNHzTI6bVVWUG2weS82JqlDQBM//W4fYOJEwEvL+DQIWD9eqfXoeKJgTgREREREZVJbhrn4ZBOnZ0Rv5OSaXfcdlmziil3MfxwVvG0mTNzzFBrsrLethXZ3+/TQNq2rGUOADcTM+wvEhAgVWbHggVOX4uKJwbiRERERERUJi14trnTY1Mi60rb99LsA3Hb+eUT9v0Md6MeiU1aAD175vi6lmy3bYDvrlFZtckO1RzOEweAF14Qh79HRQHnzuX4mlS8MBAnIiIiIqIyxTJHu571vGsb1cp74MWsud2ZDgLh9MzsfdXux+HpY1sAANenvCnNDc/t9e/bZMStRqNLc8QBwOysMnpwMNCtm7j9/fc5viYVLwzEiYiIiIiozDCbBWmNcOvh545YlhtzGIhbZcQn7l0BjdmEPTWbwtSuQ659UGdluzMM2dcN9NGhe4NK0nPrOeL30wxYd/Sa44s995z4yOrpJQoDcSIiIiIiKjMyrQqkadU5h0OW446GhlsC8Tq3YvHUqV0AgE/bDZNVW3fG111cvOpWVhG4auXc8c/rXWTnWs8RB4BJPx9zfLHISEClAv79F7h0KdfXpuKBgTgREREREZUZeqsstM7FQNxRRjwjq2r6a7uWQAkBm+s8hhOVasPDhUA8wMcNABCfJAbiGpX9UHa1q8uR+fkBbduK25s2uXYOPXQMxImIiIiIqMzYfe6WtK1W5jyX2zJ03ZIRFwQBX2z/D2/+dgJpmSa0v3gYnS4egkGpwmfthgGQF1xzxt9TK3vuKOhW5dI3GUtxuA0bXD/nIdnxbzzCZ+3AvvO3H3ZXHioG4kREREREVGYcuHRH2lbkUlTNdmj6+YQUfLH9HJbvv4KzV+/irR0LAQBLm/XCRf+qAABvN3WufdDYZOJth6EDuf9IIPPkk+Lj9u3AhQuun/cQPL/0EG4mZmD4koMPuysPFQNxIiIiIiIqM5IzjACAN3vWz7WtTgrExWHolvW8/VPvY8CCdxB69xruuPvg63bPIObtroh5uyvUqtxDLNsg+36awa6No4z4vVT7ZdQAAHXritXTzWbgk09yff2H5b0Np6VtR8P9y5I8BeILFixAo0aN4OPjAx8fH4SHh+OPP/6Qjnfo0AEKhUL2Z8yYMbJrXLlyBT179oSHhwcCAgIwZcoUGI1GWZtdu3ahWbNm0Ol0CA0NxZIlS/L/DomIiIiIiLJY5ojnVqjNuo0lI34/3YABx7fhr2+fR8eDWwEA73Qdg5ZNasHPQws/D63Ta1lT2mTib2QF+NYcLVkWn2zfTvL66+LjDz8AFy+61I+i9sPf2cXk8jT0vhTKUyBetWpVfPTRRzh8+DAOHTqETp06oXfv3jh16pTUZvTo0bh586b05xOrX2RMJhN69uyJzMxM7Nu3D0uXLsWSJUvw9ttvS20uXbqEnj17omPHjoiJicHEiRMxatQobN26tQDeLhERERERlWWWqum5FWoDAE+tOMw8VS8mDv3XrsLHf3wFD4Mex4JqY+jA9/DPo13xSb/GeeqDo6HotsxWcXiAtw6AfLkzO48/DkREAEYj8MUXeepPUbDNgOdp6H0plKdA/IknnkCPHj1Qu3Zt1KlTBzNnzoSXlxf2798vtfHw8EBQUJD0x8fHRzq2bds2nD59GsuXL0eTJk3QvXt3vP/++5g/fz4yM8VhFt988w2Cg4Mxe/Zs1K9fH+PHj0f//v0xZ86cAnrLRERERERUVlkCQlcy4j7uGgBAYroBqT+vRfjMKVBCwNJmPdF72Of4K7gZ3uxZH74emjz1QZXL3HQAsI5T76WJsdKGYzdyPunVV8XHJUuA5OQ89amw3UnVy5678vmXZvl+9yaTCT/99BNSU1MRHh4u7V+xYgUqVKiABg0aYPr06UhLS5OORUdHo2HDhggMDJT2RUZGIikpScqqR0dHo0uXLrLXioyMRHR0dH67SkREREREBMAqEFe5st63GGBXP3cCmqHPQikIWNk4Eu90GQNkBdPlXByObs2VYdlKqzYGk5ge/35vLuuEd+4szhdPTgaWLctzvwqTZVSBRX4+t9Ik95J+Nk6cOIHw8HBkZGTAy8sL69atQ1hYGADgmWeeQY0aNVC5cmUcP34c06ZNw9mzZ/Hrr78CAOLi4mRBOADpeVxcXI5tkpKSkJ6eDnd3d4f90uv10Ouzf2VJSkoCABgMBhgM9sUPigtL34pzH6lw8TtQtvH+l228/8TvQNnG+/9wZGTVp1LBnOtn76UByqUl4vOf34PWoMeOkJZ4K+J/UhAOAD46ZZ7vodlssttnew2FgznijtrZUo4ZA9WkSRC++grGUaNkfX2YEm0y4h6avH9uJYGr7ynPgXjdunURExODxMRErF27Fs899xx2796NsLAwvPDCC1K7hg0bolKlSujcuTMuXLiAkJCQvL5UnsyaNQszZsyw279t2zZ4eHgU6msXhKioqIfdBXrI+B0o23j/yzbef+J3oGzj/S9ad+6qACgQc/Qw9JccB7sWglnAJ3/MRVDKXVwoXxUTnpwKk1KeST8YvReXHecKnbqrB2xDsc2bN8ueX7yihKMBzLbtbKkDAxHp5gb1mTM4+OGHuNW0ad46V0iO3lYAyP7sYm8nY9OmzcXld4ICYz0iPCd5DsS1Wi1CQ0MBAM2bN8fBgwcxd+5cfPvtt3ZtW7VqBQA4f/48QkJCEBQUhAMHDsjaxMfHAwCCgoKkR8s+6zY+Pj5Os+EAMH36dEyePFl6npSUhGrVqiEiIkI2T724MRgMiIqKQteuXaHR5G1uCZUO/A6Ubbz/ZRvvP/E7ULbx/j8cc8/tBdLS0Da8FVoFl8+xrWLdOqjPH4Bepcb43lORprWPR7p17YQgH7c89SEuKQMzjuyRnodU9ECPHm1lbc7/eR7brttXP+/Ro0eu11fs2wfMm4fWhw/D9MYbeeqbq1L0Rvx08Bq6PRKIquVy/yXi5be2yZ5nmBRo1znCpXXXSxLLyOzcPPC7NpvNsiHh1mJiYgAAlSpVAgCEh4dj5syZSEhIQEBAAADxF0AfHx9peHt4eLjdrzxRUVGyeeiO6HQ66HQ6u/0ajaZE/IetpPSTCg+/A2Ub73/ZxvtP/A6Ubbz/RctSedzDTZvz5242A2+9BQD4plV//BtQCzX9PdCkmh9+i8kumubj7pbn+6fTZg9Nb1nBjC9HtrS7hqdb9hzqtqEVsPf8bQBw7bVefBGYNw/KbdugzMgAvL3z1D9XzN50Bsv3X8G3f11CzNsRObYVnAyzzzQrSt1339X3k6dibdOnT8eePXsQGxuLEydOYPr06di1axeGDBmCCxcu4P3338fhw4cRGxuL33//HcOGDUO7du3QqFEjAEBERATCwsIwdOhQHDt2DFu3bsWbb76JcePGSUH0mDFjcPHiRUydOhVnzpzB119/jdWrV2PSpEl5/AiIiIiIiIiyJaYZpDW7a5TPZfrqtm3Af//B5O2N7x7tCwCIbBCEAS2qyZq5a3Mv+mZLrcwOw1oFCKjgZZ9QHNKqOuoFeeOlzrXxQrtaAIB6QS4G1I88AoSGApmZwB9/5Ll/roi+cAcAcD8t9znRSenyQm0+WVnwFJsCbmVJngLxhIQEDBs2DHXr1kXnzp1x8OBBbN26FV27doVWq8X27dsRERGBevXq4ZVXXkG/fv2wYcMG6XyVSoWNGzdCpVIhPDwczz77LIYNG4b33ntPahMcHIxNmzYhKioKjRs3xuzZs7Fo0SJERkYW3LsmIiIiIqIy527WMmBeOjX8HQS/MvPnAwASBz2LVJ0YtOtUSlTxkw/D1riwJrgt6+XLFHCcLfZ202DLxHaY3LWOtNRXQrIeo388hI3Hc1nGTKEAnnpK3F63Ls/9c4WbxvUfIKyXLnutez14u4lZY9tK6mVJnoamf//9906PVatWDbt37871GjVq1Mi1wECHDh1w9OjRvHSNiIiIiIgoR0aTOCw91+D5/n1gyxYAQPrIUcD6mwAAnUaFmhU8ZU0V+ag2prJ+fRdOtwTid1MzEXU6HlGn49GrUWUcvnwXRpOAVrX87U966ing00+BTZsAvR5wMI33QeQlEE/LzB6KP/rxWvj1yDUAzIgTERERERGVepb1uNWqXMKgP/4AjEYgLAzKsPrSbm3WeS+2F4eKP982OF/9UOUxeHc0xTrDYEK/BdEY9N1+JCRn2Ddo1QqoVElcU3zhwnz1MyduGtdCyWXRsej11V4AQE1/D6iUCnjqODSdgTgREREREZUJBktGXJlLIPzbb+Jj795wU2dnflVZ502NrIe/pnbEW73C8tUPldJ6aHruKnhp7fZdu5e9TNajM3fAZLaJ1pVK4LXXxO2pU4GzZxGXmIFfDl+TPocHEeSTPUQ/Md35PPG31p+StmPviH32ygrEy/LQdAbiRERERERUJhjNYgCaY0ZcrwcsU2n79JENwTZnpaZVSgWq5VbsLQd5DcRr+HuicTU/2b6r99Jlz60Dc8n48UCXLkB6OjB0KJ5Z8BdeWXMMC3ZdyEev5SzD5QHguk1fcmMJxE/fcG2pr9KIgTgREREREZUJlqHpOc4R37kTSEkRh3W3aCEbgl1QQ6mtE/KujlLv16yK7HlKhgt9USqBxYsBPz/g4EE89btY8+u3o9dd7KlzmcbsrHpqpmufiy4reN+XVXF90d5LD9yPkoqBOBERERERlQlGKRDPIQyyGpYOpVJWjC2nIdh5oVDkLSMOAB3qBMiep1sVQAMAvdHJcPOqVYEFCwAAE6J/RscLB3HxdioyDCbH7V20PiY7mHf2o4Dt+uHqrF8grD9HuyH1ZQQDcSIiIiIiKhMM0tB0J+Gv2Qz8/ru43bu33eEWNcoXWF96NqyERlV9UN3LtfbV/eVD4W2z0DkG1oMHA//7HwDgiw2fofq9mzgYezdP/bV2Ji4JRqsAOinD8Q8Ulsy3hWVI/qJhLbK79l00fjpwJd99KakYiBMRERERUakQdToen287a5eJtbBkxNVKJ2HQwYPAzZuAtzfQsaO0e++0jlgwpBm6NwgqsL7OH9IMv7zYGrnVjXPGNgvtNCNuMWcOTlSrD199Kr757UPM+Plg/l4YwNaT8bLn8UkOqrYDGLLoH9lzSyDeJSxQ2ncw9h5e+/VEvvtSUjEQJyIiIiKiEi/TaMboHw/hyz/PI/riHYdtcl1H3DIsvXt32brbVct5oHvDSlDmN2ouBLOj/pM9z22oebJZgdG9puG2hy/CEi7huV/n5/u1j1y5J3t+KPZerm0AQOXsB5AyiJ8EERERERGVeNZZWb3BcXY4UwrEnYRB69eLj336FGTXCszKUa2cHnP2ni1+jL6MOJ8KePmJKQCAITF/AGfO5Ksf6VlBf69GlQAA207H483f5FntF5cdtjtPXYx+yHjYGIgTEREREVGJZ702dlqm4+ywNDTdUSD+33/Av/8CarWYES+GHgut4HQoe4Yx54z41bvi8mZ/12yCqNBWUAoC8OWXeXr9TKMZr645hgOXxPnlQT5u0rHl++XzvG8l6+3OL+9pvx66hbPpBKUVA3EiIiIiIirxrIuHfbfH8TrZlgJnWkdD0y3Z8I4dxeW+iimVk0jc8iODM9fvZ6/1/X3LrEJ0S5cCyckuv3bU6XisPXxNeh7k6yY7bv1jiLXp3euhfiUfzB3cxPGFBQH6W3cAQ8FUpS8JGIgTEREREVGJZx0EHruWiPMJ9gGmJZPboIqv/QWsly0rxpQ2C48/XrsCAPkPEY6cjRM/j2dbV8f+ag1x0b8qkJYGrF7t8mvHXJXP+7bNcKc7maf+YvsQ/PHy46gd6G13LPLsPhycPxRugRWBgADghReAX34B4uJc7ldJxECciIiIiIhKPNuM8KkbSXZtTl5PBAA0r1FOfuDWLSA6Wtx+8slC6V9Bsc6Ie+nU0KnFkM5kdj5H3GwWkJA1VPzZ1jUAhQKrG3QRD/7wg8uvvfCvS7Lnfh4a2XPbtc1zE375OL76/RNUTL0v7rh/H1i4EOjfHwgOBubOBUrpkHUG4kREREREVKzcStaj+9y/MOuPf11qrzeasMpmLeqYq/dlz5MyDIi9I86TblDZJiP+xx9iwNekCVCtWn67XSSs57+rVQopMDeaBdxMTMf3ey8hwWY5MetMtSWL/UuDThBUKmDfPuDs2Xz1xdfdJiOe1bd/b2b/CDKxS22H59ZLuIRv182E1mzExrptsfrP08DOncDzzwOPPAJkZAATJ4p/THkL8EsCBuJERERERFRoLt1OxR8nbuapGNfHW87g35tJ+Hb3RZfaf7f7In46eFW275hNIH46K0Nexc8d5WyLhm3aJD726uVyH4sDtVIprYmenmlC+Kw/8f7G03j0wx1ISM4Oxi1z4xUKwNddzGLf8ioPY0Sk2GDhwlxfy2gyw2ZUvHQti7RME45fu4/uc/+S9o1pH2J/Mb0eX/82Cz76VPxT9RG80msyElU6oEMHYNEi4MQJ4NNPxbZffgkMGgSkp9tfpwRjIE5ERERERIWm42e7MHbFEby65rjL59xLzczTa+w8m2C3L91mOa//4sU50vUr+cgb6vXAli3ids+eeXrdh01jlRH/epe8QN2us7ek7T3/3QYgJv21KqUUUKeOGCVuLFyY65xsvdFsN0rcx00te55uMGLDsRuyfZah8zKffopa924g3qs8Rvd7C3q1Fil6Y/ZxhQJ49VVg1SpAqxXnjHftCty9m2MfSxIG4kREREREVOh+OXIt90ZZDLkUHrPlqVPb7cuwKRx2O2uOdCWbSt/Ytg1ISgIqVQJatszT6z5sapVCWpv7rs2PFyqr9PVrv2T/CKJQKOCmVgEAkjt2BZo3F9//+PE5vpajquwBPvLPMj3TbFewTWGbRk9KkrLdXl99gcERjQAAqdaBuMXgwcDWrYCvL/D33+IPJaVkzjgDcSIiIiIiKhKZRucFxawZnSyD5YyXg0DcunCY2Szgyz/PAwD8vWyGpa9aJT4OGgSoVHl63YdNo1Q6Xc7slTXHpODctqK6TiOGgXozxGJtarWYdT582OlrGXIoBmeRlml0uoa7ZOFCMRivVw+ew4bAUyveO8vweTsdOohBeEgIMGsW7MbHl1AMxImIiIiIqFDYzgu/n+bakHPr7Gtuc8vTM03446T9sOq4pAyYsgLQQ5ezl93y99JlN0pNzV4//OmnXepbcaJQiFlxAHAUj/+cNW++ajl3AMA7T4QBANw14g8O6ZkmoFEjMfMMALNnO30t2zXCLUP8J3QKlfalG0xI0+cQiBsMwBdfiNuvvgoolfDUiX1Jyem8Rx4B/v1XDMpLCQbiRERERERUKPQ2GfB7aQaXzsswZgdlmblkx22rpVuzzFc2WwXzYdZzxDduFNfSDg4uccPSLSwZcUej+dOzssyWYd9tQ8U1x33cxCJrielZ9+OVV8TH1auBy5cdvo71jyMznnwES0eKn9fkrnWk5eBe/ikGW05l/yjSq1El+UV++gm4dg0ICgKefRZA9mgG66Hp8UkZ9nUCNPLCcCUdA3EiIiIiIioUKTbzfu+5kBE3mwXZGuC2wXxur2HNsoyWdTZXtob411+Lj888UyKHPCsUCqlquiNigTVB+oy8soqrWaqdS4F4kyZAly7iMmFz59pdZ8U/lzHshwMAAG83NZ57rCYCvN2kPtjNu88iGzYvCNmV0F96CdCJIxMs8/stfUzRG9Hqwx1o+n5UnirtlzQMxImIiIiIqFAs3CNffsxhQS4bSRkGaUg5AOgNOQfi1vPJ3TUqTO9ez66NZb540+p+2TsPHgT27BHnR48dm2u/iovRjwdL2ypFdrE2R3QaFYxmAYasbLaHRgx6fWwDcSA7K75wIXD/vuw6b6w7iUu3UwEAGpV9CGkZ6p6jFSvEZcm8vIAxY6Tdthnx2KzXAbJHQ/x29Dq6fbEHX2z/L/fXKSEYiBMRERERUaH41iYQd6VY2+0UedZcb8y5+JelSveL7Wrh1IxIvNg+BC91rg0guwCYpY0sYLTMVX76aaBKlVz7VVy83qO+tK1VK6FS5ZDJFwTZiAJLkTZLRvx+eib+Pn8bEXN240CdlkCDBkBKSo7rijsK/N1yC8Sjo4HRo8XtKVOActmjEixZeksgfv1+9nrhJ6+LIxou3krBmbhkJGRVvi8NGIgTEREREVGRyG2YOWC/DJcpl6XMpCBbq4IyK0j01IqBoaWCtyWrLgXi16+L86EBYOJEl/peXFgvB6ZTK3PMiBvNAvRWy4lZ1vT288jOiL/w4yH8F5+CId//k50VnzsXyHQ8jcB2eTLL62iNBjS6+R/KpyVK+wUBwNmzQK9eQEaG+PjGG7JzLVXTLcXaXlyWXbl9xGJxOPzNxAwAQGUnQ+BLIvs6/0RERERERIUgt+w2YL/+t8HB+tXW0jNtgmwAHlqrquDIDh7dsvZjwQLAaAQefxxo1sy1zhdDOo0SqhzmiJvM2RlxrVopBfGWjHjUqXikZn1GBpMgjg6YPl38oeLLL8XK5jaSM2ymF5jN6LzsC0zbshp+GSkAgJhKtbGqcTf4+LcH3pwK3L0LPPqoWKzNZok4R8XaLJIyjDCazNh5NgEAUNnPPdfPpKRgRpyIiIiIiIrEodh76PTZLhy7et9pG9us+cxNp3O8ZoZVRtzCPSvL+sfJOLT/dCc+23pW3K9RiWtYW4q0vfxyXt9CsaJVuZARz/o8LdlwIDvovWg1H1tspANmzBC3X39dnEefmw8/RJfffoBfRgqMCvE1mtw8h4+3fIU3pvYHLl4Uq9Jv2AB4etqdblm+LN1gcjj64bVfT0jTFR6p7Jt7f0oIBuJERERERFQoLDGiZaj4msPXcPF2KnrP/9tpRWzbrPnOs7dyfI1bWfOGLVle69cDgMt30pCcFXi6aZTAV18B9+4B9eoBffrk6f0UNzq1CsEV5MHtqxF1pG0xI26S2lp0qBvg/KKjRwP9+4trfvfuDdPqNVljzB344w/g7bcBAO91Go26r64D4uLwYYcRuFQua+myNm2A7duBAMevaamaDmTP6bdw16iw9vA16Xklv9IzNJ2BOBERERERFapHg8vb7bt8J81h29yqpFtLyjDgQOxdAEC18h7S/nKeWoft/TLTgNmzxSdvv203TLqk0aqVaFe7omzltd5NqmBgi6oAsgJxg31GvG6Qt/OLKhRisbYGDYCbN6EaNBAzt82XgnE/D4043/ubb4DevQFBwMHOT+GHlr1hUqqAwEC0XjALX369Cclxt4C//gJq1XL6ctbz3C8kpMiONavhJ3tumU9eGpSed0JERERERMWGySzAMtLYOutpcel2KmpWsB+qnGlyPRCPuXJf2q4f5CNt13JwXZXZhB6fTMvOhg8c6PLrFFc6tRK+Hhr4umtwP01cikyrVqJqOfFHCdnQdE12IK7JqdI6APj5iQH0e+9B+OILDInZArNCiXIz3kTnBTMB98jstgMGoOb8hWi7/gwGP1oNANCpXiA61Qt06T0oFAqU89TiVrIeF2/Jh8r/ff6O7Lkqh2H4JQ0DcSIiIiIiKnDWw4y93ezDjvikDIfnnbfJigKAwWSWrV9tMJnx/sbTUla9Y92KsjniFb118gsIAt7d/i3Cju4G3NzEjG8Jz4YDYtANiHPFLTQqpRSwmsxm/HlGLHRmPTRdq7YfGG0X5Pr5AZ9/jjs1aqPCxP9h6NHNwJObs4+XLy8Wdps8GRWVSiwf1Srf7yO0ohduJetx+Mq9fF+jpOHQdCIiIiIiKnCfbDkjbXs5yIinOKiSDQDf771kt892/ejVh67ix+jL2P2fOH88yFdeTVuhUGD3lA5oV6cidDBj9qbPMfToZpgVCmDFCqBt2zy/n+LIElxbF2zTqBTS838u3cU3uy8AkAffGgeV1q2DeWtXnxqMF596Hfc9skYcNG4MHDgAxMeLVdVzqNruqla1xKkLu3OpB1CaMCNOREREREQF7veYG9K2l05jd9xZIO5IfFIGqlgtXXXtXrrsuI+DjHsNf0/8OKIlMGoUcGonjAol1g+fin59+7r8usVdpax1tdVOMuLW8/D/vZkkbSsdDPF2Nlx91uYzOFDnMdxs3QG/9w8BatYskODbWrWsofTORkkMbFEVk7vWLdDXfNgYiBMRERERUYGzHkpuPT/ZIsV2PWoAZgfLVwFAgk2AZrap4m039D05Gdi7F/jxR+CnnyAolfh23CwM+OAlV7tfrM0d3AQ7/k3A0PAaAGwz4o6XNMs05jz33lFh9I/+OCMVwzPo3HIsuvYgfLIq3huz7r9WrZT1d3r3+k4L8JVUDMSJiIiIiKjAPVLFF3v+u4XQAC9ZxW4LRxnx/Zfu2O0DgPgkPRLTDXhj3Qk81bQKYBM0SkPfBQGYO1dcC/v+fem4YuFCjBs5Mt/vpbjp3aQKejepIj23nt+tUiryVdTMYLYP1C3D2gHg/d6P5PmarrId0VDeQ4s4qx9ffNztR1SUdAzEiYiIiIiowGVmrV/9cufauJ+WKe2f2KU2vth+Tlrb2yJFb8QzC/9xeK2DsXcReycVG4/fxMbjN/F822DpWPV7N9F0+1lg021g505g/37xQM2aQPPm4jzm1q0L9s0VM7aBtyofQ8eNJvmvGyevJ8qet6hpvwRdQfF2kwfa5TyzA3GNKn8/LBR3DMSJiIiIiKjASctmqZXSNgAE+Yjzmm2Hpjuqlm6x8fhN2fPkDAMqptzDp5u/QIdLh+WN1Wrgiy+AMWNKRWV0V6ht5nc7GpqeG6NZgCAIUGQtSp6UbiiQvrnCx10ellYv7y7NaTeYHE9XKOkYiBMRERERUYHTGyzrV6uQbBV0+3mIc31th6bHJTou1OWI4upV/LxyGmrduwGzQgFl69Zi9jsoCHjiCaBRowJ4ByWHbQbclQzy2A4hWLDrgmyfwSRAq1bg+LX7eO3XE9L+j/s1LJiOOmE79FylVECjUpTaIBxgIE5ERERERIVAnzU0XadWIs3BmuLWGfGkDAPGLLfJbDtR7X4cJi98C4H3biKlUlW4/7EJaFy2Am9bthlw2ww5kD0SwWJat3p4uXNtRJ2Ox4RVRwEA99MyEeDjhifn/S1r26NhpQLusZyXVh6WVvDSQaUs3YE41xEnIiIiIqICZz00vU9TsbBY0+p+UmG15Izsoc9bT8a5dM3yaYn4aeV0BN69iUvlKuH0qt+hKuNBOAAYTfJCax42ga1aqcCK0a3sznPTqPBE48qoVl5cGi72ThoMNtfqXC/Abg53QbNeTq2Clw4Tu9SBSlH65oVbYyBOREREREQF6lDsXWmtb51ahUcq+yJ6eif8/EK4NAzZerh6hsFkd43p3etlV0MHoBDMmL3pc1RJvoWL5Spj4DMfwy0k2O68ssi28F15T3ngPHtgY4RU9HJ6fgUvHQAxI77u6HXZsabV/Qqmk7l4rXs99GpUCf+83hnlPbVIzbT/TpQmHJpOREREREQFqv830dK2ZQ3xSr5i1tWyVFWy3giTWYBKqZAVc7N4sX0IRj1eCzFX76Pfgn0YdeA3dLx4GBlqLf7XZzpueZWHn3vpWls6vyr5uuHirVTpeTkP+eeiVeWcf7Ws+W4wCThzM1l2bHibovmxY0z7ENnzekHeOBOXjCp+7kXy+kWNGXEiIiIiIio0tmuIWw9zvp2iBwBcuZvm8FyVUoFHKvugzq1YTN2zFADwXufROBMgBod+nqVvfen8mPVUI3SpH4DVL4YDyC6IZ6HJJRDXSoG4GRq1fEi49aiEojR/SDP0b14Vy0fZD6kvDRiIExERERFRodGp5UuIaa0C89E/HkJaphE/Rl92er5WqcD7Ud9AYzYhKrQVVjbuJh3zKeS5yyVFdX8PLHquJR4NFtf69tTJP3MPbc7LuGmyirtlmsx29+thCanohc8GNEZwBc+H3ZVCwUCciIiIiIgKjWVouiPHryWiyYwou/2VfLMrfCtXrkCrqyeRrtbh3S4vAqW8iFdBsA2m6wZ559jekjE3mgSYzdmVykcW0bD0soiBOBERERERFRrboem2Mk3288N/GfuYuHH/PvDqqwCAL9sMxnXfAKnN3MFNCqqLpV55z5zn0mvU2UPTLVXTK3hp8fYTYYXet7KKgTgRERERERUaR4XCXutez2n7dnUqorKlQNcXXwAJCThfvioWtewjXk+txFdPN0XvJlUKobelh/XAAUUuowgs9+jzqP9wIzEDADCgRbVC6xvlMRBfsGABGjVqBB8fH/j4+CA8PBx//PGHdDwjIwPjxo2Dv78/vLy80K9fP8THx8uuceXKFfTs2RMeHh4ICAjAlClTYDTKy+3v2rULzZo1g06nQ2hoKJYsWZL/d0hERERERA+NoyAwwFvntH3jqr7iRno68PXXAIAv2z0Lg0qDXo0q4dSMSDzRuHKh9LU00ShdD/Usy8clphuw4diNrPM5BaAw5SkQr1q1Kj766CMcPnwYhw4dQqdOndC7d2+cOnUKADBp0iRs2LABa9aswe7du3Hjxg307dtXOt9kMqFnz57IzMzEvn37sHTpUixZsgRvv/221ObSpUvo2bMnOnbsiJiYGEycOBGjRo3C1q1bC+gtExERERHRw6R2UsW7R8Mg/K9DqPhkxQrg1i2gRg2EjBmKFjXK4e1eYblWACeRWuV6IH3jfrrdPn7OhStPteifeOIJ2fOZM2diwYIF2L9/P6pWrYrvv/8eK1euRKdOnQAAixcvRv369bF//360bt0a27Ztw+nTp7F9+3YEBgaiSZMmeP/99zFt2jS8++670Gq1+OabbxAcHIzZs2cDAOrXr4+9e/dizpw5iIyMLKC3TUREREREhcVdo0K6wYStE9s5PO4o29qlfiC+HtJcfCIIwFdfidsvvYSXI8PwcmF1tpRS5yGjff1+hv35DMQLVb4/XZPJhJ9++gmpqakIDw/H4cOHYTAY0KVLF6lNvXr1UL16dURHRwMAoqOj0bBhQwQGBkptIiMjkZSUJGXVo6OjZdewtLFcg4iIiIiIijejWSz45ePuOO/nKMhzt15i659/gOPHATc3YMSIQuljaZeXjLZZEOz2afKQUae8y/Pq7CdOnEB4eDgyMjLg5eWFdevWISwsDDExMdBqtfDz85O1DwwMRFxcHAAgLi5OFoRbjluO5dQmKSkJ6enpcHd3d9gvvV4PvV4vPU9KSgIAGAwGGAyGvL7NImPpW3HuIxUufgfKNt7/so33n/gdKNtK6/0XBAEGU1ZgZzY5fH9K2FdKj6hfUWqrWrAASgDm/v1h8vICStlnZFGY3wHrjHhu1/98QEMMX3JYtu/m/bRS990sCq5+ZnkOxOvWrYuYmBgkJiZi7dq1eO6557B79+48d7CgzZo1CzNmzLDbv23bNnh4eDyEHuVNVJT9+olUtvA7ULbx/pdtvP/E70DZVtruvxiDi2HGzh074Kmxb3M2UQEgOwPeoZIZ5suHsfkyoElJQeRPPwEA9jZogHubNxd+px+ywvgOhHooEZ+sRBUPAZtd+Azr+CrxX2J2Fv3omUvYbL5Q4P0q7dLS0lxql+dAXKvVIjRULKDQvHlzHDx4EHPnzsWgQYOQmZmJ+/fvy7Li8fHxCAoKAgAEBQXhwIEDsutZqqpbt7GttB4fHw8fHx+n2XAAmD59OiZPniw9T0pKQrVq1RAREQEfH5+8vs0iYzAYEBUVha5du0KjcfBfKSr1+B0o23j/yzbef+J3oGwrrfc/PdME7N8BAOjeLQJeOvuQo0LsXXx9+pD0/PFmYejRujoAQDlvHlSZmRAaNkT4pEnydbhKmcL8DnQxmrH8nytoXas8wirlHg/dLn8F7286Iz1vWi8YPbrVLdA+lQWWkdm5yXMgbstsNkOv16N58+bQaDTYsWMH+vXrBwA4e/Ysrly5gvDwcABAeHg4Zs6ciYSEBAQEBAAQf/3x8fFBWFiY1Mb2F5uoqCjpGs7odDrodPbLIGg0mhLxH7aS0k8qPPwOlG28/2Ub7z/xO1C2lbb7n27K3nbXaaHRqOzauGm1sueeuqzPwGwGFi4EACjGjIHGpl1pVRjfAY0GeLFDbZfb92laFQv/ikUFby2aVPPDS13qlqrvZVFx9TPLUyA+ffp0dO/eHdWrV0dycjJWrlyJXbt2YevWrfD19cXzzz+PyZMno3z58vDx8cGECRMQHh6O1q1bAwAiIiIQFhaGoUOH4pNPPkFcXBzefPNNjBs3Tgqix4wZg3nz5mHq1KkYOXIk/vzzT6xevRqbNm3K40dARERERERFzWjKLvzlrGBYeqZJ9lynyWq3fj3w77+AtzcwZEih9ZHs+XvpED29k8N136ng5SkQT0hIwLBhw3Dz5k34+vqiUaNG2Lp1K7p27QoAmDNnDpRKJfr16we9Xo/IyEh8/fXX0vkqlQobN27E2LFjER4eDk9PTzz33HN47733pDbBwcHYtGkTJk2ahLlz56Jq1apYtGgRly4jIiIiIioBjCaxEJtCAaicLKFlu99NrRKz4ZaaTy+/DPj6Fmo/yR6D8KKTp0D8+++/z/G4m5sb5s+fj/nz5zttU6NGjVyLBXTo0AFHjx7NS9eIiIiIiKgYMJjFjLhG6Xz5rNa1ysue6zRKMRt+7JiYDZ80qVD7SPSwcZV2IiIiIiIqMKl6I4Cc16FWKBR4+tFq0nM3pUKeDS9f3smZRKUDA3EiIiIiIioQ99MyETFnDwAgw2i/Vri1JxpXlrYD/9zMbDiVKQzEiYiIiIioQJy+kb10k8ks5NAS0GYVcvNNT0b1GW+IO5kNpzKCgTgRERERERWInWcTXG6rVilRLi0RS9e8A03cDaBOHWD69ELsHVHx8cDriBMREREREQHAf/EpLrdVxN3AL8unoNa9GxDKlYPil18AD49C7B1R8cGMOBERERERFYiUrEJtrgj6cAZq3buBaz4Vgb17gQYNCrFnRMULA3EiIiIiIioQqa4G4idOIGD9GgDAkomfQhEWVoi9Iip+GIgTEREREVGBcDkjvmgRFIIAoc9TeOPdYYXbKaJiiHPEiYiIiIioQFy7l557I7MZ+PVXAIBixHBA4Xy9caLSihlxIiIiIiJ6YKsPXnWt4cGDwLVrgJcXEBFRuJ0iKqaYESciIiIiogc29Zfj0nafJpUx6vFajhv+8ov42KsX4OZWBD0jKn4YiBMRERERUYH6bEBjqFUOBt8KQnYg3q9f0XaKqBjh0HQiIiIiIrKz9VQcZm87iwyDKc/nOgzCASAmBrh4EXB3B7p3f7AOEpVgzIgTEREREZHM2bhkvLjsMAAgVW/C208U0PJilmx4t26Ap2fBXJOoBGIgTkREREREMqsPZRdeO3r1Xo5tBUHA5hNxrl3YEoj375/frhGVChyaTkREREREMtXKuUvbZiHntltOxmHcyiPS84XDWjhueP48cOYMoNEAPXsWRDeJSiwG4kREREREJOOuVUnb+lzmiP9xUp4NbxtawUnDP7IatAV8fR+of0QlHQNxIiIiIiKSyTSaHW47YjTLj7tpnIQYW7aIjyzSRsRAnIiIiIiI5DJN2ePRc6ua7qGVl51SKBT2jUwmYO9ecbtLlwfuH1FJx0CciIiIiIhkrLPgGblkxIVc5pADAE6cAJKSAG9voFGjB+wdUcnHQJyIiIiIiGQMpuzg+25qJm7cT3fa1qV1xi3Z8MceA1SqnNsSlQEMxImIiIiISMY6EAeA7/dectguKcOATSduSs9r+ns4vqAlEG/btkD6R1TScR1xIiIiIiKSsS3QZnKyhtnkn2Ok7Ve61sHzjwfbNxIE4K+/xG0G4kQAmBEnIiIiIiIbmTYZ8SX7Yh222/5vgrRdJ8jbrnAbAODyZeDGDXH98EcfLchuEpVYDMSJiIiIiEgmOcPocP/u/27huR8O4Pr9dGw4dkN2rLKvu+OLWYalN28OeDgZuk5UxnBoOhERERERycQnZTjc/9wPBwAAr64+huiLd2TH6lXydnwxzg8nssOMOBERERERyVy9m5bz8Xv2xzUqJ6EF54cT2WEgTkREREREksR0A2LviIH21G51HbbxtJkLHuCtc3yxO3eA06fF7cceK7A+EpV0DMSJiIiIiEiSnimuC65SKtC3aVVp/783k6TtO6l62TlrxzgJsvftEx/r1QMqVizYjhKVYAzEiYiIiIhIYhLEpcpUSgXcNNnhQv8F+6Tt2ymZsnN8PTSOL8b54UQOMRAnIiIiIiKJOWvNcJVCATeNStqfmpUpd8RTq3J8gPPDiRxiIE5ERERERBKTOTsjrlPnHi7MHtAYakeF2lJSgIMHxe3HHy/ILhKVeAzEiYiIiIhIYhmarlQACoUCC4Y0c9q2Tag/+jWv6vjg338DRiNQowZQq1ZhdJWoxGIgTkREREREEuuMOAC0qFneaVt3jZMh6QCwc6f42LFjgfWNqLRgIE5ERERERBLbQNzd2fxvQDaH3M6ff4qPnToVWN+ISgsG4kREREREJLEE4kqFGIi72cwTf71HPWnbw1mQnpgIHD4sbjMjTmSHgTgREREREUnMgjwjbluIzc9dK203r1HO8UX27AHMZqB2baCqkznkRGUYA3EiIiIiIpLYDk0HgMEtq0nb7loVvnq6KZ5vG4z+zavZnQ+A88OJcsFAnIiIiIiIJLYZcQB4r3cDadtTp8ITjSvjrV5hsjYynB9OlCMG4kREREREJDGasgJxRXaQrVUr4eehAQA0reZkOLrF1avAsWOAQsGMOJET6ofdASIiIiIiKj6kdcRtst17p3VChsGEcp5aR6dl+/VX8bFNGyAgoDC6SFTiMRAnIiIiIiKJ2Sw+WmfEAcBLp4aXzoXwYcUK8XHAgALuGVHpwaHpREREREQkcZYRd8l//wEHDwIqFTB4cAH3jKj0YCBOREREREQSs1Q1PR8nW7LhEREclk6UAwbiREREREQkkZYvU+QxIy4I2YH4kCEF3Cui0oWBOBERERERSYwO1hF3yf79wIULgKcn0KdPwXeMqBRhIE5ERERERBJH64i75Pvvxcd+/cRgnIicylMgPmvWLLRs2RLe3t4ICAhAnz59cPbsWVmbDh06QKFQyP6MGTNG1ubKlSvo2bMnPDw8EBAQgClTpsBoNMra7Nq1C82aNYNOp0NoaCiWLFmSv3dIREREREQu+ePETRyMvQsAUOZlaHpKCvDzz+L2888XQs+ISpc8LV+2e/dujBs3Di1btoTRaMTrr7+OiIgInD59Gp5Wv3qNHj0a7733nvTcw8ND2jaZTOjZsyeCgoKwb98+3Lx5E8OGDYNGo8GHH34IALh06RJ69uyJMWPGYMWKFdixYwdGjRqFSpUqITIy8kHfMxERERER2TgXn4yxK45Iz9WqPATia9aIwXhoKPD444XQO6LSJU+B+JYtW2TPlyxZgoCAABw+fBjt2rWT9nt4eCAoKMjhNbZt24bTp09j+/btCAwMRJMmTfD+++9j2rRpePfdd6HVavHNN98gODgYs2fPBgDUr18fe/fuxZw5cxiIExEREREVgsOX78me+7prXD950SLxceRIIK9F3ojKoDwF4rYSExMBAOXLl5ftX7FiBZYvX46goCA88cQTeOutt6SseHR0NBo2bIjAwECpfWRkJMaOHYtTp06hadOmiI6ORpcuXWTXjIyMxMSJE532Ra/XQ6/XS8+TkpIAAAaDAQaD4UHeZqGy9K0495EKF78DZRvvf9nG+0/8DpRtxen+30zMwGu/npDt83FTu9a3Eyeg2bcPgkoF4zPPAMXg/ZQUxek7QAXD1XuZ70DcbDZj4sSJaNOmDRo0aCDtf+aZZ1CjRg1UrlwZx48fx7Rp03D27Fn8+uuvAIC4uDhZEA5Aeh4XF5djm6SkJKSnp8Pd3d2uP7NmzcKMGTPs9m/btk02NL64ioqKethdoIeM34Gyjfe/bOP9J34HyrbicP+/O6OEbfmoOzeuYPPm2FzPbfTttwgGcPPRR3EwJgaIiSmEHpZuxeE7QAUjLS3NpXb5DsTHjRuHkydPYu/evbL9L7zwgrTdsGFDVKpUCZ07d8aFCxcQEhKS35fL1fTp0zF58mTpeVJSEqpVq4aIiAj4+PgU2us+KIPBgKioKHTt2hUaTR6G/1Cpwe9A2cb7X7bx/hO/A2Vbcbr/S679A9xLlO2LaN0IPZpUzvnElBSohw4FAAS8/TZ6dO5cWF0slYrTd4AKhmVkdm7yFYiPHz8eGzduxJ49e1C1atUc27Zq1QoAcP78eYSEhCAoKAgHDhyQtYmPjwcAaV55UFCQtM+6jY+Pj8NsOADodDrodDq7/RqNpkR8qUtKP6nw8DtQtvH+l228/8TvQNlWHO6/TqOy2xdWxS/3fq1dCyQnA7VrQx0RASi5OnJ+FIfvABUMV+9jnv6mCIKA8ePHY926dfjzzz8RHByc6zkxWUNTKlWqBAAIDw/HiRMnkJCQILWJioqCj48PwsLCpDY7duyQXScqKgrh4eF56S4REREREbnA0Zrhbg6CcxlBAObPF7dffJFBOFEe5Olvy7hx47B8+XKsXLkS3t7eiIuLQ1xcHNLT0wEAFy5cwPvvv4/Dhw8jNjYWv//+O4YNG4Z27dqhUaNGAICIiAiEhYVh6NChOHbsGLZu3Yo333wT48aNkzLaY8aMwcWLFzF16lScOXMGX3/9NVavXo1JkyYV8NsnIiIiIiKDSbDb555bIL51K3DsGODpCYwYUUg9Iyqd8hSIL1iwAImJiejQoQMqVaok/fn5558BAFqtFtu3b0dERATq1auHV155Bf369cOGDRuka6hUKmzcuBEqlQrh4eF49tlnMWzYMNm648HBwdi0aROioqLQuHFjzJ49G4sWLeLSZUREREREhcBktg/Ec82If/yx+Pjii4DNKkpElLM8zREXBPu/oNaqVauG3bt353qdGjVqYPPmzTm26dChA44ePZqX7hERERERUT4YTWa7fW6aHHJ2R48Cu3YBajXAUatEecaJHEREREREZZyjoelu6hwy4nPmiI8DBwK5FG8mInsMxImIiIiIyjij2T4jrnRQwA0AcPcukDU1FS+/XIi9Iiq9GIgTEREREZVxRgcZcadWrQIyM4HGjYGWLQuvU0SlGANxIiIiIqIyzmCTEVc7y4YDwA8/iI8jRwKKHNoRkVMMxImIiIiIyjjbjLjTYenHjwNHjgAaDTBkSBH0jKh0YiBORERERFTG2RZrc5oQX75cfHziCcDfv3A7RVSKMRAnIiIiIirjbIu1KR0NOTebgZUrxe1nny2CXhGVXgzEiYiIiIjKOIPRhUB8927g+nXAzw/o0aNoOkZUSjEQJyIiIiIqwwRBQJrBJNvnsAabZVj6gAGATlf4HSMqxRiIExERERGVYWfjkyHYrF5mlxHPyADWrhW3OSyd6IExECciIiIiKsO6ffGX3T67Ym0bNgBJSUD16kDbtkXTMaJSjIE4ERERERHJqGwj8SVLxMchQwAlQwiiB8W/RUREREREBABoWbMcAGBAi2rZO2/cALZsEbeHDy/6ThGVQuqH3QEiIiIiIipcGQYT3DQqu/0mc/bk8HeeCEO/5lVx8NJdPF67YnajH38Uly5r2xaoU6couktU6jEQJyIiIiIqxQ7F3sXAb6PRuJof0vQmzB7YGA2q+AIAUvRGqd2QVjWgVSvRuX5g9smCACxeLG6PHFmU3SYq1Tg0nYiIiIioFHvzt5MwC8DRK/dxNj4ZwxcflI7tPXdb2taqHYQGBw4A//0HeHqKy5YRUYFgIE5EREREVIp56uSDYG+n6KXtV9bE5Hzypk3iY8+egJdXAfeMqOxiIE5EREREpcah2LvYfOImBNuFsR0QBAH/3kyC0WQugp49PB5a+7nhmUbxPQdXyCW4thRp6969oLtFVKYxECciIiKiUkFvNKH/N9H434oj+HLH+VzbL90Xi+5z/8Ib607m2jbDYMKy6Fhcv59eEF0tVIIgYO3hazgTlwQA8NTal4Uav/II7qdlok6gGIg/3zbY/kK3bwOHDonbkZGF1l+isoiBOBERERGVCDFX72P/xTsOjyVlGPDJlrPS8znb/3N6nRS9ESl6I2ZvE9v8fOgqMgymHF/7oz/O4K31pzB00T/56HnRWvx3LF5dcwyDvt0PAKhZwdOuzbbT8Zj+6wlpmHrdIG/7C0VFicXaGjcGKlUq1D4TlTUMxImIiIjIofUx1/HkvL24di/tYXcFRpMZfeb/jcHf7UdimsHu+JvrTuL7vZdk+zYcu2HXzmwW0PGzXWj5wXakZGZXDH9+6UG7ttZ+OXwNAHDxdqpL/b2VrMfAb6OxPua6S+0L0h8nbwIAEtPFz8nsZJj+Hyfj8Pd58YcNf0+tfQPLsHRmw4kKHANxIiIiInLo5Z9icPxaIt79/dTD7gpup2RK25YA0+Lk9UT87iDonrDqKE5cS5Ttm/hzDG4l65FuMME6Pv37/B1p3rQjyVbLfLnis61nceDSXbz8U0yezisMBhfmwAf5usl3mM3A1q3idrduhdArorKNgTgRERER5Sg5I29BaGGIT8qQttMM2f25k6JHr6/2Oj3v5Z+Oyp47CtgtziUkP0APs/qWaYTBZMbdtOwfDtp9shO3kvU5nFWwFFBI28uiYxGXmP3Z9WlS2eE5tWyLth07BsTHi8uWtWlTKP0kKsvsKzcQEREREVlRKhS5Nypk1oFtqlV2+uo9efG0Y+9EIHLOHsRlBe43El0vrvagwfLqQ1cxde1xBFfwRN3A7DnXV+6mYVl0LCZH1H2g67vM6na9tT57NMOrEXUwvlNtPFLZFxqVAj8fuoZ/bybhjR714W5bWd0yLL1zZ0DrYNg6ET0QBuJERERElCNlMRhDmWKVlU/RZxdWs53/7OOmxsAWVfHln2LV9LahFV1+jQcNxKeuPQ4AuHQ7FSEV5QXS9DbD3uMSM3DznmvzzfPK2c8mGpV4I0e3qwUAGN4mGAaTWdovw/nhRIWqGPxnlYiIiIiKs+KQEbfOgltvG03yQFyhUOB/HUPRuV4AACDTan60s7XFWwWXBwDcSnmwQDzAWydtX70rz8QfvnxP9vzxT/7EU9/sR0IhrIbm7HapHQTcDoPwu3eBffvEbc4PJyoUDMSJiIiIyM4lq+rgimIQiKdYBd96Y3ZGPC3Tfv66m0aFPk2rAAAyrdo6W1vcsnSXqxnxy3dSHS53VrWcu7R9Nl4+3/zQ5XvYeSYBgLgmuSHrB4TYlIL/bJ0VndOoXHytZcsAoxFo0gSoVavgOkZEEgbiRERERGSn42e7pO2HH4YDqVbD0Q3G7Mx2Wqbj9b+1avGfudZBqaO1xTUqBWr4i8PIE1wMxNt/usthgbgMQ87VyVcduAIA2HX2lrQvl1PyJd3JRd01Kof7ZQQB+O47cfuFFwqwV0RkjYE4EREREcmYzLbDvR9SR6xctVrLXG813DzVybJilkDcdm62LT8PLaqVc4fSbAJOHAdOnxaX7rJRwUsne34+IUWWpQeADKPjHwUstp2OR1qmEWOWH87uv/2S6A/MUbYeADx1LpSH2rdP/Aw8PIBnningnhGRBQNxIiIiIpKxXXfax03zkHqS7XxCirT9x4mbSMoQI1hnGXFd1tznUzeSnM4NB8Th6GGxJ7FpycuY/9EI4JFHgMceA86dk7XTOwiyE6yWVAOADAd9qeAlrzi++O9Y+Tmmgv+VI93JZ2JXGd0RSzZ88GDA17cAe0VE1hiIExEREZGMbSDuqXMhgCtk1uuI77twB5N/jgEApDqYIw4ASmV2gHvyehIAoJyH+IOC9Vrac31vokrfnqh/KxZ6lRqCVgv88w9Qrx7Qqxewbh1gMMCYaUDNu9dRP+EiNCZDVp+yh7ILgiBbYg0A1EoFPujTQLbvQkKKbIRBRs5J9HxJd5YR1+aSEb97F1i9WtzmsHSiQsXly4iIiIhIxmBTiTzT6DyjXBSu3k3DzUR59nn7v2LhszSrueMj2tSUts1Ww+stQ8YtVcNHPV4Lv8XcQIcLh9Br/YdQGDKxvU5rvNrtJWwe1RyVp7wEbN0KbNoEbNoEwcMDxzIyoTWLQX+cV3ksa9oTd3uFACH+AMSMve0ccaNZQHhIBTSp5oeYq/cBiEXbrBP0f8cXfF7MkhEf2SYYyRkGrDl8DQDgqEC6zPffAxkZQOPGwKOPFni/iCgbM+JEREREJGObEbd9XtQ2HL/hcH9aplHKiNf098C0bvWkY61q+UvbO88kwGwWkJY1p9tLq8IG40EsWvcBVIZMoG9fvDv0Xdx398F7McniGtpnzgBTpwKBgVCkpUFrNiJdrUOK1h1BKXcx5a9leGxIL+DsWQDAnVQxG16rgif8sjLvjav6wtddg9/GtcHrPcS+XbmbPdfdIvrinQf9iCRGk1lasm1Cp1B80r+RdCzQx835iYIA/PCDuD1+fPEoDEBUijEjTkREREQytstfPexAPCXD8fDzsLe3onp5DwBAv2ZV4WZVFVylVODx2hXw17nb+HrXBfh76ZBmMEFpNiFw6suouSQr6Bw0CFi2DNfe2gYA2HIqDkkZBvjUrQt8/DEwcyaSTv6LiO+OIN67PNRmE3qf3o1X9ixDpSsXxMzxjz8irW5rAECQKQ07Kl7GhdgEBHmFAIvPAbdvo4F3NegMWug1YtE3rdGAjhcPosmN/xBwYilgSgJeeQXo2vWBPqsMq3vnrlVBoVBg++R2uJ2SiarlPJyfeOSI+OODmxswcOAD9YGIcsdAnIiIiIhkiltG/LKDLLKFJcPs425fUM5637w/z0FtNGDOhtlwP7sXUCqBuXOBcePssr9vrDuJr55uKj5Rq5FeqzbifK5ApVTAoFBibcMu2FWrOdb+OQc1Tx8B+vRB60pVsdQtAOFXT0JhzESoTV8eA3BQ64F1DTrimk8gRv67HZXir8gb7dkjzk9v2NDlz8aWdaE2XVbl+NAAb4QG5HLismXiY+/egI9Pvl+fiFzDQJyIiIiIZOzmiJse7hzxy3dSAYhDvY9dS3TYxsfd/p+1vlaBeOVLZ/DB1q/R9OZZCBoNFCtXAv37S8ffeSIMMzacBgBsOHYjOxAHcOTyPQDyZd1ue5bDrMnz8M2/v8D8xVx43LyG9hDnYqNJE6BuXSAuTlwGTKuFYf8B+MTfxHNHNknXuOXhh921miNN44Zu148hIOEaMHEisGNH3j4gK5Zl3tw1YjbcJXo9sHy5uD10aL5fm4hcx0CciIiIiGTsMuK5rMVdmARBwOXbYnD5cf9GSNUbMXLJISSmyxfgdrTEmq+bGtXv3cRTp3bif/vXQGcyIE2jg8eG9UBkpKztiDbBUiD+SGV5RnjWH2cc9m3rubv4/dlJeN3UCk1u/oe6ty7DvWM7THljiF2W/dLNRHz40hd44sweqE0mNOnaGk8omiLJzQsA8G1iAvZ+/yIUf/4J/Pkn0KmTy59RptEMjUqBTJMZfb/eByB7HXWX/PYbcOcOUKUK0K2b6+cRUb6xWBsRERERyWTaBOIFWUwsr1L0RiRnFVmrUd4TzWuUh8ZB+W+7oemnTuH5l/phz3ejMenvldCZDPizVgt0H/GVXRBu8Vx4DQBA7QAv2drjzar7Oe3f6kNXkarzwN81m+CHlr1xp34jh4XO3HUa7AppgVd6Tsb2t+bAb9b7UhAOANd9A3Bj0DDxyTvvOH09W2mZRrT7ZCeeW3wQiWnZP05Y1ll3ycKF4uPIkYDq4S9VR1QWMBAnIiIiIgiCgGcX/YMXfjwEvcE+Ax57O/Uh9EoMxC3cNOI/XW+n6O3ayTLicXFAly6ocP5f6FVqHKwShvFPTsXz/d+Gqk5tp69Vr5KYCf8t5gaaf7AdO8+KS6RZhri/0K5Wrv31cLJWt3UhuWB/D/h6aPBatzqyNrGjXwK0WmDvXiA6OtfXAoAd/yYgLikDe/67BZPVjweCq7MJLl4Uh8IrFMDzz7t4EhE9KAbiRERERIS4pAzsPX8b207H4+LtFLvjLy47XKCvpzeaZGt9O2sTPutPAIBGpZDmPNeq4GnXVjZHfOJEIC4OQoMG+PbHnRjw7CfYWL8dBIVSNvfblpcu+xp3UzMxYvFBAEBK1lrl5Ty0+HHko2ha3Q/VyrsDAG7el69v7qlznFF212bvD8sa+l7JZjmxtAqBwLPPik/ee89pPy0S0wyYsOqo9DzZSXX5HH3/vfgYEQHUqJH384koXxiIExERERFeXhUjbb+x7iSA7KrbAHA2PrnAXitVb0SXz3ej74J9TtskphkQMWePw2PTutez2ydlxLduBX7+GVAqoVi6FBOebiO1qVXBE49U9nX6ml5u9tns9EwTUi3rj7up0a5ORaz7XxsEZQXRF21GCjjNiFt9lt5ZfbUdYm8ym4Hp0wGNRlzLfPt2p30FgD/PxsueOxopkCO9Hli0SNweNSpv5xLRA2EgTkRERFTGpeqNOBB7126/h7Zw5gv/eSYBV++mI+bqfdnQc2vrjl7D5TvZy5ZZV3KPfCQI/7zeWXquVGT1NS0NGDtW3Pnyy0CzZrLK4S1rls+xX946+yB6aXQsbiaJWe8Knlpp/8HYew6v4SwjrrYKumsHemXtk88lN5oFIDQ0+z1Mm5bjGHMF5OffScmUtn8c+ajT8yRr1gAJCWKRtt69c29PRAWGgTgRERFRGbf933iH+zvVC5S2wyoV3NrS99OyA8bv/7qEFL0Rp28kydrcTcu52JifR/accK1aKQbcH3wAXLoEVK3qcGi3JQB2xlFGPC4xA8eu3gcABFfMHhL/ROPKDq/hLCMOADteaY/fx7dBgLeYTbfPiGcF3W++CXh5AUeOAL/84vR6tjXh7mRlxIN83NCuTkWn5wEQA/w5c8TtsWPFLDwRFRkG4kRERERlnHXm2dr7fR4plNc7eT076N5yKg79vt6HHl/+hb/P35b2m8w5L5mmU2dnnj20auDUKeDTT8UdX30lBrJZXulaB21DK2BIq5znQHs5yIhvPH5T2q7pnx2Iv9zZcdE3zxxGEYRU9EKjqn7Sc41tRtyS9a9YEZg8WdyeNg3IkM9DtzDbZMuv308Xr6t2Yf3wLVvEQN/DA3jxxdzbE1GBYiBOREREVMZduGVfnK1aeXd4aNVY/WI4ACDdYCqw1/v50FVpWxAEaf751lNxSNUbcTtFD1Meli6vXV4HjB4NGI3Ak08CffrIjk/oXBvLR7WSFUxzxFtnnxW2nndtXfnc13a5tCweDoJ5Z5xmxAFgyhSgcmWxqvmHHzo8f+amf2XPF/51CYBVQJ8TyzXHjgUqVHC5z0RUMBiIExEREZVhZrOAvefETPSETqFoUs0PADCgeTUA2fPE0zMLJhA32VRKPxOXXQSuvKcW3ebuQYsPtuPaPcdZemvzn2mGkIqe+Cp2m7jcl4+PmA3PJ2fzuwGgenkP2XN/q/nismvkYV69WulgjriFlxcwe7a4/cEHwPr1duenObknNxMdZ9AlBw6IS6RpNNmZdyIqUnkKxGfNmoWWLVvC29sbAQEB6NOnD86ePStrk5GRgXHjxsHf3x9eXl7o168f4uPl846uXLmCnj17wsPDAwEBAZgyZQqMRnmhjl27dqFZs2bQ6XQIDQ3FkiVL8vcOiYiIiMipy3fTcCc1E24aJSZ0qo1Vo1tj4bAWGPV4MIDsLHBBZcTjk7KDROt53oBYNO7qXXF4tfWQcGd6NqqEHY2NCPh8lrhj3jygevV89826oJq3zXzxNqHyrLFSqcDOVzvYXSO3rLs1h1XTrQ0eDIwbJ87nHjwY+PJLwCDOnRcEARlZ96RqOXeXXxNA9o8VgweLWXciKnJ5CsR3796NcePGYf/+/YiKioLBYEBERARSU7OXbZg0aRI2bNiANWvWYPfu3bhx4wb69u0rHTeZTOjZsycyMzOxb98+LF26FEuWLMHbb78ttbl06RJ69uyJjh07IiYmBhMnTsSoUaOwdevWAnjLRERERGQRe0f8d1yN8p7QqpVw16rQNSxQKjrmnoeMeIbBhK93nce5HJY6u3ZPDLSrl/dAFT95AGkZWu0Skwn45htxKLrZDIwYkb0GdwEI8NbJnr/UOdSuTTkP++HpORQ5t+OwarqtOXOAJ54Q54m//DLQuTNSjsSg42e7YGn+5dNNnWbo7dy5I1ZLB4AJE1zvLBEVqDwF4lu2bMHw4cPxyCOPoHHjxliyZAmuXLmCw4cPAwASExPx/fff4/PPP0enTp3QvHlzLF68GPv27cP+/fsBANu2bcPp06exfPlyNGnSBN27d8f777+P+fPnIzNTrKD5zTffIDg4GLNnz0b9+vUxfvx49O/fH3MslR2JiIiIKM/MZgEnryfCnBXBmc0CRiw+CAAI9HVzeI5HVkY802SGMYeJ23Oi/kO9t7bgky1n0fPLvU7bXb8vDjmv4ucum3OdG1mF8IMHgVatxPnN6elAZCQwf759GfEHYJ0Bf75tMCr52medfd01eCzEX7avTqC3y6+hzWmOuIVGg+SVP+P1yHFI1bgBf/0F90dbotY/u6QmYZV8cPitrnihXa3cX3TpUnH98KZNgRYtXO4rERUs16tJOJCYmAgAKF9eXJPx8OHDMBgM6NKli9SmXr16qF69OqKjo9G6dWtER0ejYcOGCAzMXg4jMjISY8eOxalTp9C0aVNER0fLrmFpM3HiRKd90ev10Ouzi2kkJYnVOA0GAwyGnJe/eJgsfSvOfaTCxe9A2cb7X7bx/lNRfgf+uXQXz/5wCAAwtn0wJnepjc0n4qTjPR4JcNgPtSI7+E5O1zusLA4Ac3eck7YzTWan7+lW1tB0f08N7qZmOmxjzUOrQtf6AejZMAiGO3egfPttKBcsgEIQIPj4wPzOOzD/73+ASiUN234Qy0a0wKHL9zCwRVX8GH0ZAKBVOb9HS55rhphriXh17QlMi6wDhWCCwcVh/IJZ3k5vMDp8nauJGVjZpDv21miCnbFroNq2DfN+/xhDB76Pw1XDoDCbYDCYMbFTLXhplWhXu4Lj/goC1N98AwUA4+jREIyO13CnosP/D5Q+rt7LfAfiZrMZEydORJs2bdCgQQMAQFxcHLRaLfz8/GRtAwMDERcXJ7WxDsItxy3HcmqTlJSE9PR0uLvb/yI5a9YszJgxw27/tm3b4OHhYbe/uImKinrYXaCHjN+Bso33v2zj/aei+A4sOK2EZTDkgt2XUC/zHL48qQKgQJifGW43j2Hz5mN25wkCoIAKAhTY+Mc2+DgYAW0wAy2uncXTx7YiLP4iFIKAy9sCkdK6Oe7WrYvkGjWkbPWhK2I/EhNuIDETyG2A5v/q6lHN4yr0/15ASt83UO6cGPBfbd8ep0aMgN7PDyjg6Yu1ABzccxaWfyqfOnsBmzPP5XjOK3UBY+xhbI51/XUyjNmfLQCc/vcMNif/a9cuLg0A1LhSrhJ+7fwC6l2+hYZnj2LlT6/j7a5jsWVLdkBdA8DlGPGPrQonTqDNuXMwurlha7lyMG7e7HpnqVDx/wOlR1pa7oUmgQcIxMeNG4eTJ09i717nQ4+K0vTp0zHZqupjUlISqlWrhoiICPj4+DzEnuXMYDAgKioKXbt2hUbjeBkMKt34HSjbeP/LNt5/KqrvgN5oxsvR22X76j/aHhei/wYAfPHc4wiu4OnoVADA60d2IC3ThMfadciuHn7vHpRr1kBx9Chu7N6P/udPyU+6fRk4egAAIDRsCNOkSRCGDMG7H+0CYEDDeqGIvZOKU/fkRX1t9ezaEVXLuUP5yitQnTsHoXx5mFauRFCnTgjK06eQdxP3bwMABAfXRI8e9Qr8+gaDATfSorD1jh/OxKUgpHYd9OgYYtfuwq1UzDom3qv2kd3wlb4yrn/wCrr9F42Pt3wFs3ccTF9+Cfj7250ruXcPqpkzAQCKYcMQ0a9fgb8fyjv+f6D0sYzMzk2+AvHx48dj48aN2LNnD6pWrSrtDwoKQmZmJu7fvy/LisfHxyMoKEhqc+DAAdn1LFXVrdvYVlqPj4+Hj4+Pw2w4AOh0Ouh0Orv9Go2mRHypS0o/qfDwO1C28f6Xbbz/VNjfgTfXH7fbFzH3b2m7ZkUfaNTOM9PuGhXSMk0wCAqxn6tWAf/7H3D/PgCgKgCDUoVfGnTGttqtYVCp0e7OBYzGNWDvXihOnIB65Ehg2TKUDxmIe/7VoNOo4a61f8/t61TE7v9uSc8r+LhDs22bVOlbsWwZ1JGR+fwk8ubdJ8Kw6sBVjO1Qu9DuTy0foIVXOZyJSwGgcPg6KlX2XHpBoUKK1h1j+0zH2P1rMfmv5VCvWQPlnj3AwoViYTdbN28CnTsD//4LeHlB9dprUPG/OcUK/z9Qerh6H/MUiAuCgAkTJmDdunXYtWsXgoODZcebN28OjUaDHTt2oF/Wr2xnz57FlStXEB4eDgAIDw/HzJkzkZCQgICAAADiUAwfHx+EhYVJbTbbDJWJioqSrkFERERErvv50NUcj2tzCMKB7CXM4o+cQr11PwCLFokHwsKAp57CpJh0HKz2CK75Zk8t/Kd2Czz/Xjco798Tq5t/8AGwcye27N6DlU26oetj47HxfDwqpKpw27McAODsB92gU6vw65FrmLz6GBSCGV6LFwGvvCJedNw4oEePfH4KeTe8TTCGtwnOveEDsixjpjc6LoZnMGUXccs0mpGeaYKgUOLr8IH4q2ZTbDj4nRhkP/kk8Nxz4ue0Ywfw99/AhQviMUBcquy334Dgwn9PRJSzPFVNHzduHJYvX46VK1fC29sbcXFxiIuLQ3q6uAyFr68vnn/+eUyePBk7d+7E4cOHMWLECISHh6N169YAgIiICISFhWHo0KE4duwYtm7dijfffBPjxo2TMtpjxozBxYsXMXXqVJw5cwZff/01Vq9ejUmTJhXw2yciIiIq/Xo3EdeKbhtaAY2r+cmOfTGoSa7n309Ow8S9K9CmV9vsIHz6dODYMQjvv491DTrhmm8gvHRqHHi9M1RKBTKNZiQk64Hy5YHXXwdOnkRm957QmE147sgmVO7dHS+8MRz75z+Hfid24PHaFaBTiwF/RFggnow/gW1rpkPxv/+JldF79QI+/7wgP5Zio6K3OPE+zmqNdWsGq2r1eqNZtqZ7l6E9gSNHgClTxHn4S5cCjz4q3p+NG7OD8Lp1xectWxbeGyEil+UpEF+wYAESExPRoUMHVKpUSfrz888/S23mzJmDXr16oV+/fmjXrh2CgoLw66+/SsdVKhU2btwIlUqF8PBwPPvssxg2bBjee+89qU1wcDA2bdqEqKgoNG7cGLNnz8aiRYsQWUTDkIiIiIhKE1VWobT2dSqiajn5NL8+TavkfHJmJr5d8SYm/r0KasEMISIS2L0bwsyZgFoty9auGNUKAT5uqJS1FNq1e1ZFi2rVQuziVXhm0Af4O7QFEBKC5AqBUAtmfLTlS4Qf2y22u3IFXr174csl01H70inA2xuYO1fM5GpdXCu7hKmctTTazfuOA3GjOTsQf3LeXtnQ/ba1/QE3N+CTT4C//gLCwwF3d3FpsrlzxUJ2V68CZ86IS5YRUbGQ56HpuXFzc8P8+fMxf/58p21q1KhhN/TcVocOHXD06NG8dI+IiIiIHMjMyqiqVQpU9csOxOsFubDm9ccfo+3lY0jRuuP1yPEY991bWL7/MpZN34zeTSpj5lMNpaZ1s67n667BtXvpSNHLl8e6k5KJfTWbIL5lG+x4pQOW7vgP1Sa8gN7/7sb/vpoG/P41cOOGuAyZTgeMGiVmdqvk8mNBCeeuFUcCZBgdL3tm/WNHWqa8jUpplVdr0wbYt6/gO0hEBe6B1hEnIiIiouLPmBXIaVRK9G1WFd/uuQhAzGDnKCFBzLQCeD1yHH4Pa4/MqP+w5ZS45Oz6mBuo5Jsd2FvmOrtnzSnPsFlP+06qHgDg7ylOR3TTqvFqz4lI8CqHEUc3QX1ZXLcbbbOGwNetm9+3XKKoleKIBZPZcdLLaHK8P7yWPxpW8S20fhFR4WEgTkRERFTKWeYYa1QK1A3yxuE3u8BoFuDvZb/ijMz77wMpKTgeFIoN9dsBgBSEW3yz+wIAQKkAVFkBpaW425c7zuOHv2Px3dDm8PPQ4m5qJgDA30scYq7TqGBQaTCz0yjcmTgFr/ndB+rVA2rVKpD3XVIoFTkH4tZzxC0qeGmx6oXWhdovIio8DMSJiIiISrFUvRE7ziQAyM5Y5xqAA8D582K1cwCzOoyAoMi5tJDl2gCw/+IdAMDpm+J6ut/uuYhp3erh36zn5T2zAnGrau3lqwYC7R5z5S2VOpaPzuxkGqijQNyFGaNEVIzlqVgbEREREZUs83eel7bVqjz80++NNwCjEejWDRNnvphrc63VtY02md37aZnYdioOqw6Iy6hZfgiwLk7WNrSi630rZXLLiNt+ngDAOJyoZGMgTkRERFSKnUtIkba1KoVrJ+3cCaxeLS6H9fHHaFXLHx/1bZjjKZoc1iJfdeAqXllzTHrun5URfyzUX9oXVtnHtb6VQpYh/U7icOgdFHFLz3Rc2I2ISgYOTSciIiIqxZRWsbda6UIORq8HxowRt8eOBRo1AgD4uGtyPE1jFeSH1/JHdNbwdIvkjOwK6pah6S1qlMP6cW1Qw98j936VYqpcMuKpevugu34lFyreE1GxxYw4ERERUSmWlJ4dACtcSYh/+inw339AUBDw4YfSbksldGc8tNn5nUXPtcixrWU+uUKhQONqfvDzKJ3rg7tKmUPV9MR0A9787aT0PLyWPwa1qIZPBzQusv4RUcFjIE5ERERUSqXqjbLMtLOMqyQpSQzEAWD2bMA3e2ksnUb+z8Y1Y8Jlz7102YG4p06N/z7oji+fburwZSp6l+3A21ZOy5d9tvWstD20dQ2seqE1Pu7fCCEVvYqsf0RU8BiIExEREZVSvx+7IW2X89Cgfd1cCqJ9+60YjNevDwweLDtknRFXKIDm1cvJhpR7u8lnPGrVSjzRqBJe6lxbtn9SlzpoVr1cXt9KqSYVa3NQCj32Tqq07aHLeVQCEZUcDMSJiIiISqEzcUmY/usJ6fmhN7tCp84hkDMYgC++ELenTAFs5pO7a7PPFQRxOPX3z7WU9tkG4oA49HxM++w1wZ9qWgUvd6kNhUtj5MsOafkyBxlxtdUkfy8tyzsRlRb820xERERUypy8nijLhreoUU6qzO3Upk3AjRtAYCDwzDN2h90cBPHW64B7uzku5pbb3HLKOSNuveRcoI9bkfWJiAoXA3EiIiKiUuTq3TT0+mqvbN+ox2s5aW3lhx/Ex2HDAJ3O7nAFb/t9blZBtvUccWvW2W+DyZx7P8ogVQ5zxK2r0Qf5MhAnKi04NJ2IiIioFNl5NkH2vEEVH3RrEJTzSTdvAps3i9sjRjhs4qVTo6bNMmNuVgXcdDmsI25hNOVSLK6MslRNN5sFCIKA/604jPaf7sS91EyorKYIVGIgTlRqMBAnIiIiKkXeXn9K9rx7g0q5n7RsGWAyAeHhYqE2J2pW8JQ9t55zrnUlEDczI+6Iympo+q6zt7D5RBwu30nDzM3/YoPVFANmxIlKDwbiRERERKWYn4fjudsSQQAWLxa3R47MsanKpsia9bBpNxfmgld0MLydsoemZxjMOBOXLO1fe/iarJ2zefhEVPIwECciIiIqweKTMtDxs12Y9+c5u2O+7hpEhOUyLH3/fuDMGcDDAxg4MMemttXOFQoFhrSqjnpB3niycWWn5y0c1gJd6gfg1Yi6OfeljLIupPfxljMO2zzbunpRdYeIigCLtRERERGVYOtjruPS7VR8tu0/jOsYKju2Z0pH+OaWEbcUaRswAPDxybFp17AAbP83Hr7u2dec+VTDXPvYNSwQXcMCc21XVqlcWM3N00kxPCIqmfg3moiIiKgES8kwSttJVtsAcg/CU1OBn34St3MZlg4AA5pXg5+HFk2q+eW1m5QDZW5LywEA69wRlSoMxImIiIhKkMt3UuHnroWvhwZpmUZ8+ed56VhSukHaXjy8Ze4X+/lnICUFCA0FHn881+ZKpQKRj+Qy1J3yzHbuvSNmB2uME1HJxUCciIiIqIS4ejcN7T/dBQDYPaUDDDbLgV29lyZtt61dIfcLLlwoPo4aBbgQDFLhsJ1774iDJcaJqARjsTYiIiKiEmL/xTvS9uTVx5BolQEHgBGLDwIA3DUqaFS5/DPv5EmxUJtaDQwfXtBdpTzwdss9N+bK8nBEVHLwbzQRERFRCWE9Ovnw5Xt4f+Np2XG9UVyn28fdhUGPlmz4k08CgSyk9rB1b5DzkH8PF5aHI6KSg4E4ERERUQlhO0845up9h+3qV8q5+jmSk4EffxS3X3ihAHpGD0qdywgGdy0DcaLShIE4ERERUQlhcDJReMWoVrLnuWVX8d13wP37QN26QNeuBdQ7ehCaXNYwa1rdr2g6QkRFgoE4ERERUQmRYrM8mUWb0Aqo4e8hPW+c0/Jiej3w+efi9tSpgJL/HCwONLnch+Y1yhdRT4ioKPC/vEREREQlRHKGwemxxcNbQqkAqpZzR50Ab+cX+eEH4MYNoEoV4NlnC6GXlB/qHDLirYIZhBOVNly+jIiIiKgEEAQBX++6YLf/q6ebAgBqVfRC1OT2qOitg1LpJKjLyABmzhS3p08HtNrC6i7lUU5V7t/r3aAIe0JERYGBOBEREVEJcOFWirTduKovuoYF4rnHasLbTSPtD6nolfNFFi0Crl8HqlYV1w6nYsPZHPHlz7dC3aAcRjgQUYnEQJyIiIioBLiXlj0svXP9QIzvVDtvFzAagdmzxe3p0wGdrgB7Rw9Kp3ZcFb1t7QpF3BMiKgqcI05ERERUAhhMZmn7+bbBeb/AL78AsbFAhQrAiBEF1zEqEN5uzI8RlSUMxImIiIhKAINJXLqsfiUfeOryGLQJAvDpp+L2+PGAu3sB944elPUUAyIq/RiIExEREZUAxqyMeG7rTTu0axdw+LAYgI8bV7AdowLBjDhR2cJAnIiIiKgEsGTEc6qu7dTHH4uPI0aIQ9Op2CnvmV3BvmEV34fYEyIqCvzpjYiIiKgEsMwRVztbmsyZHTuArVsBtRqYPLkQekYFoWXN7LXC24RWQIreiPAQ/4fYIyIqTAzEiYiIiEoAo1kMxLXqPGTEDQbg1VfF7f/9DwgJKYSeUUGwvq8ZBhP+fKU9FIp8TEMgohKBQ9OJiIiISgCDURyanqeM+KuvAjExgK8v8NZbhdMxKjDjOobA112DEW1qMggnKuWYESciIiIqAQxmS7E2F/MoixcDX34pbi9dyrnhJcCUyHp4pWtdKPM6/YCIShxmxImIiIhKAGNeirX98w8wZoy4/e67QO/ehdcxKlAMwonKBgbiREREREXoflomRv94CFtO3szTeSl6IwAXli/LyACGDgUyM4E+fTgknYioGGIgTkRERPT/9u47PIpq/+P4eze9kEZIgwRCC71IDdKUDoKK/hTFXlDU61XsXbHgVS9eRdTrtSuKHRsiXVrohN4SSigpJCG9Z+f3xyabLCmApEE+r+fJw+zMmZkzO5Ml3z3nfE8devKn7SzalcjdX24+430SM/J4/c+9wBm0iL/8MuzfD8HB8OmnYNafeyIiDY0+mUVERETq0PztCbbl0inJTue3bWWt501cnaouuGtX2Zzh77xjTdImIiINjgJxERERkXryyA87zqhcXmGxbbl7aBXBtcUCU6ZYpyybMAGuvLImqigiIrVAgbiIiIhIPfm9XOt4ddYfTAUgxNuVsV2DKy80Zw6sXg2entbWcE1/JSLSYCkQFxEREakj0UfSqtyWV1jMdxuPkJSRV2FbWm4hAC9c3qXyMeK5ufDkk9blp5+G0NCaqK6IiNQSBeIiIiIideS7jUcqrCsdJv6fxft55PttXPe/tRXK5Jd0TXdzcqj8wG+/DUePQlgY3H9/jdVXRERqhwJxERERkToSEdSkwrp8C6yMSeb9v2IBiD2RXaFMbkkg7upUyZ9uWVnw+uvW5enTwc2t5iosIiK14qwD8RUrVjB+/HhCQkIwmUzMmzfPbvstt9yCyWSy+xk9erRdmdTUVCZPnoyXlxc+Pj7cfvvtZGVl2ZXZtm0bgwYNwtXVldDQUF577bWzvzoRERGRBqSw2ABgQvcQ23zgBcVw22f2U5nFJNn/XZRnC8QraRGfPRtSUqBtW5g8uRZqLSIiNe2sA/Hs7Gy6d+/O7NmzqywzevRo4uPjbT9ff/213fbJkyezc+dOFi1axG+//caKFSuYMmWKbXtGRgYjR46kZcuWbNq0iddff53nn3+eDz744GyrKyIiItJgFBRZ+6E7O5ptQXVGYcVyw2f+Zfc6r9C6X4UW8fKt4c88A46ONVthERGpFWf9aT1mzBjGjBlTbRkXFxeCgoIq3bZ7924WLFjAhg0b6N27NwCzZs1i7NixvPHGG4SEhDBnzhwKCgr4+OOPcXZ2pnPnzkRHRzNz5ky7gF1ERETkfJJfZG3ZdnY04+7sQGZeEQcyT5/dvMoW8fKt4ddfX+P1FRGR2lErY8SXL19OQEAAERERTJ06lZSUFNu2qKgofHx8bEE4wPDhwzGbzaxbt85WZvDgwTg7O9vKjBo1ir1793Ly5MnaqLKIiIhIrSttEXdxNOPubG0PySysPBA3DGs3dovFIL+otEW8XCCemQmlQ/fUGi4icl6p8U/s0aNHM3HiRMLDw4mNjeXJJ59kzJgxREVF4eDgQEJCAgEBAfaVcHTEz8+PhATrXJoJCQmEh4fblQkMDLRt8/X1rXDe/Px88vPzba8zMjIAKCwspLCwkj5fDURp3RpyHaV26Rlo3HT/Gzfd/8Ynr6AIAEeTNRgHyCywbuvbypes/CJ2xWcCkJtfgJODmcy8Itv+LmbD9ryY//MfHFJTMdq2pej//g/0HJ139BkgegYuPGd6L2s8EJ80aZJtuWvXrnTr1o02bdqwfPlyhg0bVtOns5kxYwYvvPBChfULFy7E3d291s5bUxYtWlTfVZB6pmegcdP9b9x0/xuPfQfMgJm4gwfIyDABJtadsAbkWWkpXNvawnPx1j/Pfpu/ABcHOJkP4IiDyWDJwj8xmcAxO5sRr7+OA7B5/HiOLlxYT1ckNUGfAaJn4MKRk5NzRuVqvQ9T69at8ff3JyYmhmHDhhEUFERSUpJdmaKiIlJTU23jyoOCgkhMTLQrU/q6qrHnTzzxBNOmTbO9zsjIIDQ0lJEjR+Ll5VWTl1SjCgsLWbRoESNGjMDJyam+qyP1QM9A46b737jp/jc+q+bthMRjdOrQnj+XxNhta9UimGsmdOW5zYsBGDpsOD5uTgx6YwWQj4uTI+PGjQLA/PzzOGRmYkRE0O2VV+jmUMX84tKg6TNA9AxceEp7Zp9OrQfiR48eJSUlheDgYAAiIyNJS0tj06ZN9OrVC4ClS5disVjo16+frcxTTz1FYWGh7YFctGgRERERlXZLB2uCOBcXlwrrnZyczouH+nypp9QePQONm+5/46b733ikZlu7LDZt4sr0yzvz7M87bds8XJxwc3XBbAKLAZgcyCgwSMywDr3LKSi2PidJSfDWWwCYXn4ZJ1fXOr8OqVn6DBA9AxeOM72PZ52sLSsri+joaKKjowE4ePAg0dHRxMXFkZWVxSOPPMLatWs5dOgQS5Ys4fLLL6dt27aMGmX9Brdjx46MHj2aO++8k/Xr17N69Wruu+8+Jk2aREhICADXX389zs7O3H777ezcuZNvvvmGt956y67FW0REROR8cywtF4BQP3du7N8SX/eyP9j8PKxJap1Lxo7nF1k4klpJF8eXX4bsbOjdGyZOrP1Ki4hIjTvrQHzjxo307NmTnj17AjBt2jR69uzJs88+i4ODA9u2bWPChAm0b9+e22+/nV69erFy5Uq71uo5c+bQoUMHhg0bxtixYxk4cKDdHOHe3t4sXLiQgwcP0qtXLx566CGeffZZTV0mIiIi5y3DMDh60hqIN/dxw2Qy0bdVWU+/SX3DAHB2sP55VlBs4el5O2zbP7ixF+zbB++9Z10xYwaYTj/1mYiINDxn3TV96NChtuk0KvPnn3+e9hh+fn589dVX1Zbp1q0bK1euPNvqiYiIiDRI6bmFZOVbM6C38HUD4KmxHchMTmD65MGE+3sA4OzoABSx6fBJW8b0OwaGM7JzEIy73ZodfexYGD68Xq5DRETOnSacFBEREakDpa3h/p4utvnAg71dubaNhZZ+ZTO8OJqtrdyPfr/Ntm5S3zD46iuYPx+cnODNN+uw5iIiUtPOumu6iIiIiJy90kC8tDW8Kk+M7VBhXcCSP+Dmm60vHnoI2rev8fqJiEjdUYu4iIiISB04kWXNfh7QpOIsL+WF+JQF6t2P7+XBVV/h9a9N1hXXXw8vvVRrdRQRkbqhQFxERESkllksBsv2JAHg4VL9n18hPm60SEvguSX/Y0TMOutKBweYOtXaJV1zhouInPcUiIuIiIjUsl+3HWdpSSDu5lxNIG0YNP/sA5Z/+jiO+XkUm8zET7iaFv9+Bdq0qaPaiohIbdMYcREROSv/W3GAFftO1Oo5dh5P5z+L95FXWFyr5xGpK79vi7ctl05PVqnXX4cHHsAxPw9j6FDytkTTYt43CsJFRC4wahEXEZEzdufnG1m0KxGAQ6+Oq7XzjHt7FQD5RRYeG10xcZVIQ7FgRwLH03K5bWB4teUigpqwsPR3JyW78kILF8ITT1iXX30V06OP4qF5wkVELkhqERcRkTNWGoQDddJavWR34ukL1aAvog6xJia5Ts/ZEGw4lMrGQ6n1XY3zTl5hMXd/uYnpv+3ixo/WnbZsqYT0vIoFYmJg0iSwWOCOO+DRR0FBuIjIBUuBuIiInJHNcSftXsckZdX6OVOzC2r9HKX2JGTwzM87uf7DdaTnFtbZeetbRl4h//d+FFe/H6WhAGfp/q+32JZX7k/GMIwqy2bll723j4yKsNvmkpqK47hxcPIk9OsH77yjIFxE5AKnQFxERE7rRGY+E99dY7fuslmriEvJqfFzrdpf1iKdU1B3geG2I+m25UPJFbsO5xYUM+mDKGYt2V9ndapN2flFPPHjNuauj7OtO5GZX481qjnVBcQ1aeEu+x4bpdOTlVdsMdh0+CRfl7zPt17cimEdA8sKpKUROX06poMHoXVrmDcPXKqf3kxERM5/CsRFROS0qmr9nvzR2krXv7N0P3d9sZHCYstZnedgcjY3lOviW5eB+KM/bLMtJ1USkK7cf4K1B1L596J9ZOad/y3m86KP8fX6I7wyf49tXWXXfb6576vNjHxzBflFtfvsVPZsj5+1ih3H0u3WfbDiAFe9V/YlVtfm3mUbc3JwuPJKvA8dwggMtI4RDwqqtTqLiEjDoUBcREROK6OKwDMnv/Jg542F+/hzZ6LdmPIzMWLmX9Vut1gMjp6s+VZ4i8W+BXVvQkaFMnlFZYHXsbTcGq9DbVl7IIWkjIpjktNyKt7T5EpadM83v22LZ39SFlGxKbV6nsreq8SMfG78aB0Wi2FrlX93eYxdmSt6NLcuGAbcdBPm1aspdHen6LfflBldRKQRUSAuIiKnlVHFmOmTOfZjuAuKLEz6IMr2+vhZBqxFlopdiovKtTx+FnWIgf9axvX/q7wl/u8qnd+51I5jFQPx9HLXmpjRcAPWI6k5tvH8N3y4jkkfrGVsSRb60znfx8aXf1Zqu3d66TMQ7O3KO9f3tK0/mVPIPXM203/GEpIy8sjMK7Jt69XSF7O5ZOz3e+/BDz9gODmx9qmnoHv32q2wiIg0KArERUSkSusPpjL4tWX8Wm4OZICNTw8HwGLYtyYv35vE2gNl2bdnLbVvDTwdD2eHCut2xZcFxS/9vhuANbEppOWcfSK3jYdSiT1RsZt9YqZ9i3FOJUnLTpZrQT5c1fRT9SwmKZNBry1j4rtrWLn/BKtKMsAnZ+XbBalgHSN+qnlbjtVJPWtLdrmhDAa1G4mX9jII8HJlbJdgPrutL22aeQCwYGcCiRn5DDulh0f7QE/rQmwsPPIIAJYZM0jt3LlW6yoiIg2PAnEREanSK/N3E5eaw4p9J2zrnhvfCWfHsv8+Ci3WAC87v4h/Ldhjt//ZtLDmFBTZAqlPbulDzzAfAF5bsJev18dx9GQOLXzdbOUrG7f+4+aj3DNnE8/9vIOkU4LrQ8nZXP1+FMP+/VeFruhODvb/HeZUEqSW78rdUAPW4TNX2JZv/Gi93ba0U+7Fkt32vQDA+gXH+eqhb7dy7X/LemPkF55dfoKzkZSZx5QvNgHWVniz2cSQ9s3o37qpXbnyreEA/cKbWqcnu+02yMmBoUOx3HdfrdVTREQaLgXiIiKN3C9bj3PJG8srBLYnswuIPpJmt+7+S9ty68XhOJrLplaavSyWVo//Tv9XlhB7omJL8ZlOiZWcaW3hdnE0MzSimS2QWhWTzBM/bmf6r7vspjM7dZz28bRcpn27lfnbE/gs6jB9X17C63/uobgk6F5Zbn7wA8n213pqwJSZV8Rj32/j7XIZ0su3gm+OSzvrRHT17dRrPJ5uff8eGRVhN53W+TT+vVR+UTE/bD7KnoRM27qsSr5MOVeGYRCXksN/Fpc9FwFNyjKcTx1a9RjvPq18uaxbMLz8MqxYAR4e8PHHYNafYiIijZE+/UVEGrHcgmLu/3oLB5OzGX5KN9pvNx6pUL55SYu0Y7ngoTRYzSwX+Lx5bXdKY/Xy48sPJmezt1ywBIBhYLEYHE61BrrNmrhgMpm4a0hru2ILdyXaBZOnTrWVklWxq/rsZbFc+u/ltHr8d56Zt8N2vnV7E9h4KNXWMl6aeKtziBcAexMz+WbjEWYu2ofFYhCTlMmSknHkzbJSaZV6jJ83HK5wvoasfKb3omKLrWv6Zd2CuXNQ2Xs9YdaZjSdvSCrreVFZ1/tz9e7yWAa/voyv1pVN+Tb98i625Ra+7qx5/NIK+90yoBXfjW+J4803wbPPWlf++98QHl7jdRQRkfODY31XQERE6s/Wo2l2rxPS8wjydgUqdtcG63hY6zZThW3ltXMu5rG1c+m7dwNNP0iCggKMggIc3f3Ic3ajuIUvDunpcPIkpKeT6tMMi3dznmjWCn9vN0hbxITgYH7fl0SSpx9dEmIIP3mcLGd3nIsL8SzIwdM0DPrcA66uGIZBWq41EHcqLiTsZAJtUo/SJvUofjnp+OWkE5qeSEjGCZpln8TptWJ2BrZm4+UTiXjift5bHgtA50BPLNHRuBXmk+rmRY6TKzkLFnLkjzVMX72Jbgn76BFv/eKh4EsP6N8XOnSA9u0hIgIGDoQmTc7pnpytncfTueq9NUzoHlJtuYzccl9iZOVjMcDBbKKFrzsO5Xo4pGSf/dj7mnbgRBafrTnEXUPaEOLjdtry6ZVkgK+qRTy/qJipX27mojAf7ru03VnV6/U/99q9njq0DaF+7nbrQnzcePPa7jz+5Xo6nDhEl8RYnty4H5YtsXZLN5nglVfgrrvO6twiInJhUSAuItKInTwl6DqRmU+Qtyv5RcW8esp4byjrhmsymXAwm2zdvsu7JXkbnYfcTJcU+/HGJiA0vWQ6sxOH7Lb5pyYyJDWRIQc3W1csspb/oLrKb5kP703naN9BfJ/ThLC0BBYkHqBt6lEcLafvDt81MRY+eJ2iT9/i85DOGCYT/T6NxzXRPjEd78Ilp+yb6+iCW242LFtm/Snl4wP/+Af885/QtCl1YVxJRvRvNx6tttwNH61j8zMj8PNw5sFvogEothjWILywkCEHNtH3yA5apiVQsPpfOIe3guHDITIS2ra1BpB15P/ejyIlu4AjJ3P5+JY+py1/6vh3gKwqptZbsS+ZpXuSWLonidsGhuPufGZ/CqXnFGIy2Wdjv7RDQMWCu3YxdvabjP7sC9yKTsmuP3gwvPEG9Dn9NYmIyIVNgbiISCN2agBzLC2Xri28mbloHwUl82aP7x7Cr1uP4+bkYNf653hKIL7knr60eeNF+GgWAEeCWvF2z8u54o4JXNyjFQPf+IvAjGTcCvPpFuDGrlwz3iGBrE8uICTjBBHJh2mXHEfrAC+GtPWD48fJjjlIRuxh4r382RzSAdeiAgodrP91XXloPT4pibRY9BsPnHphHh7s9g5hj09zEps0JcPFgyPegRz1DiTJ0w+LycTgg5uZvO1Puh3fx+BDW2y7WtzcOerchGbZaTgXFxLnE0hM01BimobRc/QAlrfswQe7M4lIPkz/9CP4Hz9Eh4x4Lsk+ivnwIXjxRZg5E+65B265BTp2rNEgdtvRNNo08+Sl33fZtWSXN+eOfkz+cF2F9bOW7ufpcZ04kmodB+7u7AC7dsGkSXy2fbt94VUr4YsvrMtNm0L//tYW/2uugdatqU2lrfKnTitXmYy8Qv7v/agK66vqml6+i/6xk7m0aeZJXlExi3YlMrpLEC6OZZn7s/OLyC+y4OfhzMqYExWmRAsu6T0CQH4+PPUUzJyJS0nBZHdvirr3IGjMMLjuOusXGiIiIigQFxFp1E6dB/zuLzdx6NVx/BJ93LauX7gfD49sT16hBS9XJ9t6Jwcz+SXBeltzHm2unQBrS+b3njaNFzpMZHFsGg6GPzuPGhz1CuCol7UFcRWAB1AAeEG8VzM2tegEwG0XhzNkvHXZo+Rn4JPzbUH/hO4h/LL1OBs7P80TTTP54eUPCchK5bhXM3YGtuHWu8czaFgvdm4+xsPfbbXV98mxHWhXaGHmon0AfNN9FN90H0W35AN0PLafXq2bcc2VkZgvvpg/1h/n1fm7cLQUU+hQds1f3dCP4r1JWPbmsDugNbsDWkO7IQDMubU3F29faU3GFR0Nr79u/QkNhZEjoXdvazDbo8dZ36dvNx7hnaUxXNEjhLdPMyXc4PbNGNCmKT1CfdhxLJ2IoCbsPG6dAm7BjgS+2XCEnIJiMAy+c9gBvSdCbi7Fvn5836IX+5q1JNXNi2daWfDbsh42bYKUFPj9d+vPE09YW8lvvBFuvRVcXautz9k6de75fYmZtA+surv/txvscxmE+3twMDm7yq7pCRll2fRHvGnNMu/t5kR6biH3D2vHtBHtASgstnD57NXEJGWxeNoQ4tOs+43vHkJcag5FxRaCvUu6zW/fDpMnW/8FuPxydl1zK7/7tefBERFQyTAPERFp3BSIi4g0Qpl5hXi6ONpNyVXqkjeWE59uDTqauDhyfd8wzJW0vDqWjBNvmp3GR989BYmHwdcXvvwSxo7F/4dtEJvG3A0Vk75Vx9fdqcK6qy9qwTcbj9C9hTcD2/nzy9bj/L7zBL8DDLrBruzjXSPAZMLHrew41/cLY8rgNizelVjh2Nv8W7PNvzWR1/aAns0BuGtIGyb3b4mniyP5RcW8tzyWomKD/q2bsvZA5VN83ffNVrY8ezVcdRXMnw/vvGPttn7kCHz0kfUHYNQoa6Kus5g7+tHvtwGcNggH8Pd0xmQy8fntfUnNKuCDlQdsgXjpffXKy+KVP2fTec9K604jR+Lw+ec89uZG23FS2zfjs3fetLb0bt0KUVHWQHzJEutyVJS1m/UXX8CAAWd8LZUpKLIw+cO1OJrNRJ3y/u6Oz6g2EC/v0dERtGrqwT1zNrPreEalZRLT8yqsK0329vaS/bg7O3DnoNZ0fvZPCkoy4w+f+Rd3DLQmVgv2duXtST0wDDBjwMw3rV9OFBRAs2bw4YcwYQKdgE5n+gaIiEijo0BcRKSRiUnKZPjMFUy8qDkOJV2mpw5tY0tYdjC5bJqu3+8fVGkQDtbM6QGZKXz8w3RaJh6GFi3gzz+hkzX8uGtIm7MOwgF8PJwrrHtoVHs6hXgxqnMQGw+nVrlv31Z+tqCtZ5gP4f4euDiaeWCYNSmXl1vFIL/UyM6Bdq89Xaz/Rbo4OvDA8Pa29a7ODlTmZOmXGiYTjBtn/cnJsU5VtXy5tZV8yRLre7R6NfzwA1xy6ujzilKy8k9bZsrg1nyw4gBQlpTNy9UJL1cniovL+lN752Zy8+bfuD76D4KyUsHR0Zo47KGHwGzmxSu62LLLlwbvuLhA377Wn3/+E+LjYe5caxB+4AAMGQL/+hc8+ODf7oK/OiaZDYdOVrrtn3OjubxH8yr3dSk3p/3UIW1s92FvYibJWfn4e1rzGsSn57LjWIZdi3hlXv1jD6/+UZYfwbmokJ7H9+D73q88fPIkQw42wfSLI6asLNi2rawV/LLLrEF4YGAVRxYRESmjQFxEpJH5cOVBAH7cfIwRnaxBQ/MqMlP7elQduA7dv46nvv0XvnmZZPs0xWPJEmv28BLh/h70DPNhS1xapfu/MKEz3Vp4c+W7a+zPWUmLeEATV24e0AooGddciXn3Xkz3Ft621009XVj28FC7MuX3fXR0BK8tsGbBvjmy5Rkn7QpoUtYV+/5L29q1UhcWW+yzzbu7w+jRfOjRHq+rnLhmdgHccYc1MB83DvOrr2Jq2bLKcxmGYes+XZ3Sadeg4thov4wUbtvwM/2ObGfA4a00KbB2/U4NaYnfT99aA+wSN/QLw9PFgQe/2Vp1b+rgYGvQfccdMGWKNSh/6CHIy4MnnzxtXStTWYb+8k5k5tOs3Hzd5ZVOm3d1rxaYTCb8yn2R8+A30Xxxez8Axs9aRXIlU9xVpf2JQ1y39U+u2bYIj8Jywfupw9E9PKw9HKZMqdOEdiIicn5TIC4i0si4OpUFo6XjcZs1ceGJMR2Y8Yd9pvTSVuEK/vyTV798FkeLhR2BbTj5v08ZVC4ILxXs7UppGrTWzTwY2Nafz6Os82/3b92UiKAmLHt4KP9becA2N3OQV/Vjjv08Kg/IeoT6VLsfWAPW2weGE+bnzs0DWnHbxeGsiU0msrX/afctNTSiGd5uTjT3ceOeS9ryj2HtaPfUHwBEH0mjTys/W9ms/CL6v7LENl65+wODifjzT7jtNpgzB4eHH2aUtzfm5cutQWyI/RRkx9PzSK1mOrEmro60D2zC8I6B3D+sHe8ui+HhUeXuw48/8tCDt+GYkW5btbtZKz7oO5EBj97F//W1v2cmk4lB7ZoBkJSZT0GRBScHE0dP5uLp4ohv+d4KTZrAV19Bz57w2GPWRGUODtag3PHs/rxYuCvB7nWPUB8+vbUPPaYvAuC6/63l+7sj8XGv2FuitAdAZc/qyv3J7DiWTpfm3hQkpxKek06hgyNFJgdGtmqCS3EhnZt78em6IzzZy4+fv1lGm5SjXHR8t22aOoATHj6sa9GFE56+jO7diuDgpuDpac2SP348BFSSPV1ERKQaCsRFRBqRxIw8vl4fZ3td2v24U7C12/cdg1rT5sn5ALg6mTFV1sK3ezdccw2OFgvzOg3hkbEPsG1UZKXnczSXtXS+eHkXNh0u635c2vId7u9Bx6CyMcC9WvpWew3lW0b7tvLjip7NGdTuzAJpk8nEM5eVjdx1dXLg0g5n15XY39OFdU8Ow8nBbMta3reVH+sPpZJ4Srfn4f/+yy5p2A0frWPDU8Ph889hwACMF1/EJSEBZs+2jrX+17+sLasl79vOY9YA2snBRGFxxanitj470jZ0YNqI9tw9pLW1Zf/YMWtwPGcOjkBOp6683awX60K7EB3SHsNk5v9C/CocD6CphzMujtZEfIkZefy67bit50D0syPIKSjm9T/3MrJTIKO7BGF69FHIzISXXoLHH7dexxtvwOjRZ/yeln45A9Yvb+bdezEA47oF8/u2eGKSsugxfRGvTuzKpL5hdvseKhlK0cK3rFfHqM6BxKzYSJ8jO9k98X06p+5hW0zV4+uvKPm3b7l1RWYHHC6fwGfdRvN8boittfvGl8co+ZqIiJwzBeIiIo3IlM832jKdlxdY0grtYDbxwoTOPP/rTib1CatQjpQUawtgRga723bn0TEP0CW8GW5VdBfPLyqby3lAm6bsji9LoFU+oP6/3qEcTctlRMfAyoP/cgKauODm5EBuYTG3DQxndJegasvXhvK9CgB8Sr5UODX53anjkU9kloz3NpvhnntYPvAyPnvxEx5c/RXd4/fB1KnWQPbzz6FNG2JOZAFwWbcQXri8M71fWmybVs56GPv3yj0jDd56C958E7KzrcHjww/j/vLLxHwVzZbd1unAWvi60b2KHgQmk4kmrk7kZ+Uz6LVldtuW7kli7YEUftpyjJ+2HOPFyztzY2QrmD7dmqjshRdg504YM8b6M3MmdOhQ7Xu5J8E+qZpHuZZtz1OGCzz+43baBHja9To4nJoDQJtmntYV27fzr6+n4/PHrxXOleHsjktxASbA2dOjLON7YSH4+UFEhPWnUyccL7sMAgMZlprD8yXvw+tXd8NRQbiIiNQABeIiIo3I1qPpla53Lpfw6uYBrbjyouY0ObWrb0EBXH01xMZCq1Z4/DqP6/dlc8/QNlWer/y8yyaTiat7tWDOujgu7RBgF3C7OjnwxJiOZ3QNTg5mfrxnAIdTchjVuWEkxvIt6TL99LwdHE7J5qlxncjIq5iRHmDgv5by870X4+3mxK1fbYc2vVkR3pPlbjsJ+/fLsGYN9OkDX39NerH1y5CmHs54uTqx+ZkR7EvMZOqXm7gpshUUF8PKlfD999ZM7QcPlp0oMhJmzYJevQD49zU9+GN7PMM6Btoyq1fF1anyYHPat1vtXj/z805rIG4ywf33W6c0e+kl63n/+AMWLYJ774XnnrNm1K/EzIX77F7f2L9szPzk/mF8s9E+4d/KfSfsAvH0kin4go/GwrP3wXff4QNYMBHVsivbA9ty+ZQrGbHDhSwXdwCu7R3Kv67uVuX1lxfq586+l8awOz6DbuVyEIiIiJwLBeIiIvUkPaeQ+LTs0xesB+XnCwesEfU//mFNMubpCb/+SliHVjxffWMnp3am9nF3rpBA7e/oGOxFx2Cv0xesI+Vbpv+38iCBXq58vKosKJ7UJ9SWQf7oyVzmrIvjkoiyccUWswN7rrmVsNsnwzXXwLp1GGPGMGrgGFI929EyYjSkN8fTMLjIks7atqmY5n4H1/4EJ07YV6ZnT3jmGbjiCrvkYd5uThW6dVfFzanyHg6n5etrTVx2113w8MPw66/w1lsUffElr1zzGBdNncxl3ezHwZfvRfDv/+vOFT3LMqR3a+HDwgcHs2xPki1/wcGUnHI7pzFsza+M3baUiNd2lH3zc+21/LvfNcxOtPa6+DzJlSwXa+8Ekwku72lfh9NxdjRX2YNARETk71AgLiJST4bN/IvkrHzu7mBibB2d08vVkYw8+6zat5RkI6+SxWJNJPbBB9Yo5uuvoUuXMzqfYVQc13whGtYhwG7s/Uu/77YtN/dx49WrutlN5VZYbCH5lGnJUrMLoHMYOYuW8PPAiVy3bSEXrZzPRQB/vAW3lZW1a8v284PLL4eJE63zeftVPvb7bDic0uX9yp7N+WnLMdvrUD83jqRaE/0VFFnselQA1uz5v/wCixZRdP8/cdyzm2fff5RPNqyENd+TaTExa2kMg9s1I6fQ+jx+cksfLulQMelZ+8AmtA9sgr+nCy9/toJevy+HtZ/Cnj1Yli3jxcJyPQ8mToTnn4euXXkEOPTVZn7fFs/xkrnDHc0mFk8bQit/j3N9i0RERM6JAnERkXpSGoi9v8eBO3IL8XeqeqqwmpJbaB2z7exopqDIQrC3K89P6Fz1DgUFtgzfgLW187LLzvh847uHsHh3Eq2aup9LtRu84Z2q7iL/5R39KqzLyi/iZI59NvTUktdJ+fDEmPv5pvsoRu9dTc/je+l1IhbH/JLx5g4O0L079O9vbfUeOhRq+NnZk5BpW45s3ZQZE7sysK0/D31n7ZreL7wpCenHKCw2SM7KJ6SS6e8Mw+C/zq2JffITOr31Mrdu+pVbN/3K7k59+faxmXwSm8viXYlYSr6s8XSt5k+Skyfp9f5rrP34PZwtZV8kmYE9/i1Z2Wckd7z7JKZWrex2O/W5K7IYCsJFRKRBUCAuItIAPPbjDj66pe/pC56D/KJiW+btr+/sx0erDvLA8IpTjtlkZlpbGBcvtk5H9eGHcPPNZ3XOCd1DaNbEhU4NqBt5bXn/hl488v1WMsv1OOjTypfwksBv7pT+TPpgLQDxaXmEeNsHrwnpeXy74YituTs6JILokAgAfpjSl17NvaxJ3sxmazBeR9oHeuLq5MDg9s1s6/5xaVt+iT4OGFz93hrWPDGswn4r9yfzaul0eMPvYkX4Rbz1y+t0jN3Kg/eNx63/1XzaazwOXtaM+XbTj504Ye3Wvnkz7NoFUVG0yrN+EbEtqC2rW/YgoUlTVrXqQWzTUO69pE2FIBxgSPsAZi+LrbH3QkREpKYoEBcRqQdFxfaZy5fsOVFFyZrz69Z423L3Fj68O7lX1YUTE2HsWGsg5OEBP/wAo0ad9TlNJhMD2pz5HN3ns9FdghjdJYi8wmKGvr6chIw8+rduatvev3VTPrixF1O+2ER8ei4r9lsDdjcHg9xik90UXmCddz0jtwjDMOjYwhec6+6/7E7BXuwqyXA/qrM1K31TD2e6tfCmqNigha87BSXPcGm379yCYtYeSGFA26a4ODqQlGnf9X5Zmz5cedO/+c+vb9A1MZZHV3zOlPU/Mj9iIAlNmtI8ZwlknoQdO6zPncX+d6SoS1fubn8Fi9tV7GFw15DKEwb2DfdjyuDWRMelAXDlRc0rLSciIlLXFIiLiNSD7Pzi0xeqQek5hTz8XVnG62qnYNq8Ga66Cg4dsk5JNX8+9O5d+5W8QLg6OfDZbX1ZuDOBOwa1tttW2oW7fPb6i4MMFh+rmME8IrAJM6/pQaHFYp0bvA59emsf/tp3giERzQhoYp3iy2w2Me+ei23L3Vt4264jM6+Q1xbs5Yu1h7mhfxgvXdGV7PyiCseNbRrKhJvfZMKuv3hg9VeEn4zn+q0LrBtXnVL4ootg+HDrdGJ9++LYuTP7Xl8OqTl2xbY/P5ImpyYXLOfJsWeWjV9ERKQuKRAXEakHmfmVT21VXkxSJrd+uoF7h7Y942zXlbFYDLpPX2h7/cSYKlKdGwa8/z488IB1bHjr1vDnn9C27d8+d2MVEdSEiKAmFdYHe7tWWNfKs/KEdv6eLrg5O+BG3XVDLxXg5cr/9Q6tsL58dvj3b+xF5IylAHy78ShfrLW26H+5No77LmnHyv1lvTx6tfRl0+GTABgmMz93voTfOg5mzN7VdDhxiKD8TK4e0sGaaK5DB+jbF8IqPvPf3hXJi7/v4vdtZb07qgvCRUREGioF4iIi9SAxI7/CuriUHMLKJZd69Y89HEnN5fEftzO2W3CFKcV+2Xqc+LTcKrvlloo5kWX3uk94JVm1MzNhyhSYO9f6esIE+PTTKud+lr/Hz8O5wjpPp8oDcR/3hh1gBpcb456VV0SYnztxJa3V/10RS/SRNADuGtKaey9py8/Rx3lm3g7bPsVmB37rOJjfOg6mbYAnV08bctpzBnm7Mvv6i+gQuJ9/L9rHG//XvWYvSkREpI5U0zdRRERqQ35RMVe9t6bC+sGvL2Pmwr2V7vPir7sqrLv/6y3M+GMPO46lV7JHmV+3Hrd7fVHYKcH17t3Qp481CHdwgDfegHnzFITXApPJxA9TI22vb+wXimcVX4nXdXf0v+P6fmWt1uHlspEnZeaTnGXNAn/vJW3xcnXixv4tGd/dOn93q6buXBTmYyvfLsDzrM5799A2LHt4KFf3anEOtRcREak/CsRFRP6mvMJivlh7mN0lSa3O1B/bE6rc9vbSGNtyC9+y1vHvNh21K1dsKWtFvWzWKlbHJLNy/wl+2XqcJbsT7crOKnfMnuWCH4qK4M03rd2A9+6F5s3hr7/goYes84VLrejV0o+Yl8fw3d2RPDaqPR5VNHx7uNR9l/Sz5eZkrWNuYTGF5RIQln455Oxopkm5bOiPjorgH5e25cd7LsbVqez6ruh5dknUnBzMdoG/iIjI+abhf90uItJAvfrHHj5dc4geoT7Mu/fiM94vMSOv2u35RcXM23KMT9ccsltfbDFwKBmjm11gnwhr8ofrbMuOZhPbnx+Fm3PFQO71q7tZFzZuhNtvh23brK+HDoVvvoGAgDO+Dvn7HB3M9GnlR2FhIW5VxNsujg3/u3JbIF5QRE5BWQLCwynWLuoFRRZM5b7UCfVz56GR1inZ0nLK8iSMrGYedhERkQtRw/9fXkSkgYqKTQGwjYU9UyfLBSAzruzMkGALN/Uv6+L72PfbeOyH7RX225eYCVjnm96XkGm3zbG4iKGxG3jrl9eJemsyjmEtrEmvhg/nnqhvaX/iEO+MCKXtsRiYOhX697cG4U2bwv/+B0uWKAivJ+U7H/xzWDvbcvn5yBuq0i97Pos6XOnvQd9WleQjKJGSXZYnwaQeGCIi0sioRVxE5G+yGJUn2apOXEoO7/8VC8D9w9px9UXNcU/YytixHfh8bRwA86KPV7rvmLdWcuCVsVw+e5Ut2ZtfTjovxy2lz6Lv8c8pN1Y8B0hMgL17eRR4dMXn8PEpB7z2WnjnHfBvHPN8N2Rf3tabQ6l53NC/JW8t2Q+Ai9P50zW9Kh/f2qfKbSklY8hFREQaIwXiIiJ/U/HfCMRv+WS9bTnQy+W05Qe182flPus0UD55mfzfvf+lddpJhqcepXv8PibsWYlroTUoT3b35teOg/m9w0BynNzwzsuiY0Y8g3evoe/RnbgX5oGnJ1x6KUybBkNOn6Va6ka/cD8GtrcOFn/7up4s3pXI/50HiciCKpmOrTxPl6r/zHh4VASv/rGHWwa0quFaiYiINHwKxEVE/qbK4vDU7AJ83Z2q7GqbX1SW0GpMl2C7bYPa+bNyf7Lt9fujwhi9+meOv/cWQZkpmKki8O/dG+Oxx/jetyuvLoqx2xRFNz7uOorWzTxY+s+B1qzo6gbcoE3oHsKEkuziDV2PUB+718HerrwwoTMPfhPN66eZWmzKoNYMbOtPh0rmWxcREbnQKRAXEfmbyndN33k8nWd/3smmwye5f1g7po1oX6H820v2cywtF4Avbu+Ln4czhYVl48U/urkP7Z/+g4gTh7h14y+M+s9fkJ9P+ZAsxc2LNDcv4nwC2d80jJj+l/DaW/dhMpm4G8g3zLy5eF+Fcwd7u4KjPvKlZp06L/qlHQIY2TmI7c+Pwmyu/gsfs9lEl+betVk9ERGRBkt/lYmI/A0nswtsmaEBxr29yrb89pL9PDCsXYVAZOaisgC5qccp3dKLi3H+cz5z5j7LxYe3la3v3Rv++U/eyAvif3syyXe0Bj6XRDSjdTNP7uoXZtfC7epUeQ5OL9cq5sgSOQeup4wRf3JsR4DTBuEiIiKNnbKmi4icRnZ+ERaLfbfwni8uqnafhbvK5vL+eNVBxr610m57K/+yOcIDN2zAsVMnuOIKLj68jSKTmc39hsPq1bB+PdxwAyl+AbYgHGBoRADPXNaJNs087Y5bfsqyYR3KsqCXn+NZpCYN7xiIn4czm58ZgUc1Y8JFRESkzFkH4itWrGD8+PGEhIRgMpmYN2+e3XbDMHj22WcJDg7Gzc2N4cOHs3//frsyqampTJ48GS8vL3x8fLj99tvJysqyK7Nt2zYGDRqEq6sroaGhvPbaa2d/dSIi52j70XQ6P/cnN328/vSFy9l6NM22PP23XeyKz7C93vT0cNydrQGL6eef6ffKK5gOHgQ/P9Lue4BfflxJ19V/woABttbu4nJfBPzrqq7c2L9lped1cij7WJ86tI1t+dHRHc6q/iJn6oMbexH1xKUVuqmLiIhI1c46EM/OzqZ79+7Mnj270u2vvfYab7/9Nu+//z7r1q3Dw8ODUaNGkZeXZyszefJkdu7cyaJFi/jtt99YsWIFU6ZMsW3PyMhg5MiRtGzZkk2bNvH666/z/PPP88EHH/yNSxQR+fvGv2Ptcr4qpiyJmlFubHhVwcd7y2PtgudSQV6uNPUs6Za+fj0ON92EyTCw3HgjHDmCz6w3mXjFALuAGqB8g/a1fcKq7PqbklU2N3PPMF8OvTqOQ6+Oo32gEmJJ7TCbTbg4Nvyp1kRERBqSs+5DNmbMGMaMGVPpNsMw+M9//sPTTz/N5ZdfDsDnn39OYGAg8+bNY9KkSezevZsFCxawYcMGevfuDcCsWbMYO3Ysb7zxBiEhIcyZM4eCggI+/vhjnJ2d6dy5M9HR0cycOdMuYBcRqQ+p2WXzHy99aAg9plfeTf2nLcfoekoyqqERzawLO3fCuHGYcnNJ6NWLpv/9L2Y3tyrP2T7Qs8pt5Tk7lgXwDhqnKyIiItIg1egY8YMHD5KQkMDw4cNt67y9venXrx9RUVEAREVF4ePjYwvCAYYPH47ZbGbdunW2MoMHD8bZuayladSoUezdu5eTJ0/WZJVFRKqUV1hs97p0nPW6g6kAhPt74ONe9jkV0MSF6Zd3tr1++LutjPrPCrtj9Aj1gfnzYdAgSE7G0rs3Gx9++LQZzW+5uBX3DG3DD1Mjqy13fb+WjOkSxKzrep72+kRERESkftRoVpWEhAQAAgMD7dYHBgbatiUkJBAQEGC33dHRET8/P7sy4eHhFY5Rus3X17fCufPz88nPL+uSmZFhHY9ZWFhoNz1QQ1Nat4ZcR6ldegYarpTMfLvXbyzYw8Mj25GcaZ2CrI2/O4WFhbTwdePoyVweHdmOy3uEkJlbwOsL91c4ntlSzCVzZsHbbwBg6duXvB9+oHjTptPefzPw4DDrmO/qyrqY4e1ru522nDQM+v0XPQONm+6/6Bm48Jzpvbxg0pvOmDGDF154ocL6hQsX4u7uXskeDcuiRdVnYJYLn56Bhic+B8p/TP535UH8Mvaz86QJcCAjJZH58+dzdxs4kmXC4Vg0849H45pnvx9AUEYyny18ncDYnQAcHDOGHbfdhmXTJkD3v7HT/Rc9A42b7r/oGbhw5OTknL4QNRyIBwUFAZCYmEhwcLBtfWJiIj169LCVSUpKstuvqKiI1NRU2/5BQUEkJibalSl9XVrmVE888QTTpk2zvc7IyCA0NJSRI0fi5eV1bhdWiwoLC1m0aBEjRozAyUnz/DZGegYarg2HTsLWDXbrZmwt+9js0KYVY8dWno3cFBLH1xuO0NzHjd55iUx94VEckpIwPD0pfucdWlx/PS3Q/W/sdP9Fz0DjpvsvegYuPKU9s0+nRgPx8PBwgoKCWLJkiS3wzsjIYN26dUydOhWAyMhI0tLS2LRpE7169QJg6dKlWCwW+vXrZyvz1FNPUVhYaHsgFy1aRERERKXd0gFcXFxwcXGpsN7Jyem8eKjPl3pK7dEz0PBkF1qznvcI9SG/yMLuePsPVm935yrv2W2D2nDboDawfz8MuRKSkqBrV0w//IBju3YVyuv+N266/6JnoHHT/Rc9AxeOM72PZ52sLSsri+joaKKjowFrgrbo6Gji4uIwmUw88MADvPTSS/zyyy9s376dm266iZCQEK644goAOnbsyOjRo7nzzjtZv349q1ev5r777mPSpEmEhIQAcP311+Ps7Mztt9/Ozp07+eabb3jrrbfsWrxFRGpbWo41O7q3mxOWSqYia+J6mu8yDxyASy+F+Hjo2hWWLoVKgnARERERaVzOukV848aNXHLJJbbXpcHxzTffzKeffsqjjz5KdnY2U6ZMIS0tjYEDB7JgwQJcXV1t+8yZM4f77ruPYcOGYTabueqqq3j77bdt2729vVm4cCH33nsvvXr1wt/fn2effVZTl4lInUrPtSbb8HF34lharm19h6AmFBRbuLpXaNU7x8VZg/CjR6FjR1i8GPz9a7vKIiIiInIeOOtAfOjQoRhGxZahUiaTienTpzN9+vQqy/j5+fHVV19Ve55u3bqxcuXKs62eiEiNScspCcTdnBjXNZi3luynbYAnCx4YXP2OeXlwxRVw+DC0bw9LlsAps0WIiIiISON1wWRNFxE5V4XFFqb/uotgH1fuGdrW1iLu7ebEPZe0IdTPncHtzqBV+5//hC1brC3gixdDueSVIiIiIiIKxEVESny38ShfrD0MwK0DwknKzAOsSdlcHB24uleL0x/k66/hgw/AZIKvvoLQarqvi4iIiEijpEBcRKRETFKWbbnjswtsyz5uZ5jFNCYGSnNZPP00jBhRk9UTERERkQvEWWdNFxG5UH28+mCl67u28D79zvn5cO21kJUFgwbBs8/WcO1ERERE5EKhQFxEBPhw5YFK13cP9aF9YJPTH+Dxx2HzZmja1Nol3VEdjkRERESkcgrERaTR2xJ3kpd+313pthY+bqc/wO+/w3/+Y13+5BNocQZjyUVERESk0VIgLiKN3vqDqbbl2weGc/+lbW2vfT1OMz78+HG45Rbr8v33w/jxtVBDEREREbmQKBAXkUZv7YEU2/L9w9oxoG3ZFGV+Hi5V71hcDDfcAMnJ0KMHvPZaLdZSRERERC4UCsRFpFE7kprD8n0nAPj01j54uznR2t/Dtt3LtZqx3v/6FyxbBh4eMHcuuFQTtIuIiIiIlFAgLiKNWmJGHoYBTT2cGdK+GQD+nmUBdV5hceU7rllTlhn9nXcgIqK2qyoiIiIiFwgF4iLSqGXmFwEQ7OOKyWQCwGw2MWVwa3zdnbiiZ/OKOyUmwnXXWbumX3893HxzXVZZRERERM5zml9HRBq1RbsSASgostitf3JsRx4f3QGz2WS/Q1YWjBsHcXHQti289x6YTikjIiIiIlINtYiLSKP21bo4APYlZlXYViEILy62toRv2gTNmsEff4CXV11UU0REREQuIArERaTRKiwuawXvGHwGAfWDD8Jvv4GrK/zyi7VFXERERETkLCkQF5FGa+uRNNvy3Dv7V1/47bdh1izr8hdfQP/TlBcRERERqYICcRFptA4mZwMwqJ0/3u5OVRf85Rd44AHr8muvwdVX137lREREROSCpUBcRBq8AyeyePi7rcQkZdbocY+l5QLQwtet6kKbNlnHhRsGTJkCDz9co3UQERERkcZHWdNFpMF7e8l+5kUf5/tNR3lhQmcOp+TwyZqDrH9yOM2auJz+AFVIzsoHoJlnFcfYtAkuuwxycmDUKJg9WxnSRUREROScqUVcRBq8NbEptuXnftnJx6sPYhhw9ftrzum4JzJLAvFTg/ljx6yt3/37Q0ICdO0K334LjvruUkRERETOnQJxEWnQDMMgp6C40m2HU3J46qftpGYXnPVxP1gRy587rXOI+5e2iOfkwDPPQLt28L//QVERXHUVrFihacpEREREpMYoEBeRBm3hrkSy8osAmHhR8wrb56yL47M1hyrd90hqDjuPp/N51CF6vbiITYdTAZi7Po5X5u+xlWvWxMWakK1zZ3jpJcjNhYsvhlWr4Pvvwcenxq9LRERERBov9bMUkQbtri822ZZnXtODmdf0ILegmI7PLrCtXxWTzIMj2gOwYEcCLXzdsBgGE95ZbXesz6MO0zHYi8d/3G5b1yUhhs63zoTlS60rQkPhzTdh4kSNBxcRERGRWqFAXEQarGV7k2zLY7sG2ZbdnB1wdTKTV2gBYNPhkxiGweqYFO7+clOF45TaE59J4sFjXBqznktjNzD44GbC0q3d03FygocegqefBg+P2rkgEREREREUiItIAzZnbZxteeY1Pey2Te7Xko9WHbS9Xn8wlW82Hqn0OG2T47h10y8MittG2LTjfFxum+HggOm662D6dAgPr8nqi4iIiIhUSoG4iDQ4uQXFPPPzDhbvtrZW3zKgFa5ODnZlHhkVgYujmXeXxwLw+/Z4ft16vKyAYdDvyA6mrP+RYbEb7PaN9WvOwS59GP7oHZgGD4YmTWr3gkREREREylEgLiINzttL9/P9pqO21/cMbVOhjKuTA4+O7oCPuxOvzN/D51GHrRsMg2Gx6/nHmm/oEb/Puspk4s92kcztNpItIRGkuzXhqotaMHxc9zq5HhERERGR8pQ1XUTO2C9bj3Plu6tZdyDl9IX/pjWxybxX0soN8MSYDgR4uVZZ/tIOgQA4FhcReXgb3815jI9+eNEahLu6wt13Y9q7F8/ffmZ5m96ku1lbv4O9qz6miIiIiEhtUou4iJwRwzB4+NutFBRbeO3PvfwwdUCtnGfxrrIEbd1Dfbh9YDXjti0W2qxZzCffvURk3HZci0rmE3dzg/vvh2nTICAAgIGn7Orpqo8/EREREakfahEXkTMy4489FBRbs5SfyMyvtfOkZFuP7e3mxLx7BuDoUMnHVHExzJ0L3btjuvJKLjmwCdeiAjJcPDg4cTLs3w+vvmoLwkt9emsf23IzT5dauwYRERERkeqoSUhEqpVXWMzkD9ex6fBJ27qcgqIaO/6xtFzeWx7DXYPbEOjlytqSbu9vTeqB6dR5vAsLYc4cmDED9lnHf9OkCTuuuIFpTp3Z7x/GN3dfTHhzv0rPNTQigLlT+rMmJpkrejavsWsQERERETkbCsRFpFq3frLBLggHOJlTiMViYDabKt3HYjEwAIcqtpf3wNwtbDh0ki/XxtGmmQeJGfl4uznRv3VT+4IrVsDUqbBrl/W1nx888ADcdx9Nza4UfLCWsc296d3St9rz9W/dtOKxRURERETqkAJxEalSYbGFqHKJ2fq28mP9oVSKLQbxGXlsiTvJ0IgAPF0cScnK52ROAcNnrrCVX//UMAKauJJfVIyLo0OF48el5LDhUFmQH3siG4AXJnQum65s61Z45RX49lvra39/ePRRuPtu27RjwcDyRy6p4asXEREREakdCsRFpErfbjxiW3Y0m3h+QmfGvr0SgItfXQpAZOumPDo6givfXVNh/7UHUvkl+jgr95/g5/supkOQl22bYbGwdO1eWp48jl9OBr65GXgW5PCPS9vRbusJ+OUwLFgAa9eWHXDKFGu3dL/Ku56LiIiIiJwPFIiLSJWe+mmHbXn/y2MqjtkGog6k2AXhXnlZtEk5SuvUYxxfN5ehaSlcnZOG5ZscCPSEjAxISqI4OZlbiou55dQD/nrKawcHuOoqeOIJ6NGjpi5NRERERKTeKBAXaeB2Hk/HMKBLc+/Tlt1wKJUHv4nmur5h3HtJ2wrbM/MKueGj9YzvFkxuQTFHT+byysSuFcZyp+UU0GP6ItvrWdf1tAXhk/qE8lNULIGZKTTPOEG75MO0T46jXcoRWqccpVlOWtUVjClbLP3wyXR2w+LXFOegAFz8fDFjWIPvsDDo0gUmTYKgoNNeu4iIiIjI+UKBuEgDdvRkDuPeXoWbkwNrnxiGt7tTteW/WhfH0ZO5vP7nXv6vVwsCvFxt27YdTeOxH7azOz6DrUfSbOu/2XiEnmE+fH1nf1ydHDAMwy4IBxjXNdi6sH49r858kFei1mI2LFXWozAohPUu/hz2CSHDpykZXn4cMntw9yVt+TD6BDF4kOzuTZqbFwWOTjw8sj33Xdru7N8gEREREZHzkAJxOa8VFVt4c/E+Lm7jz4C2/vVdnRo1d30cj/+4HYDcwmJWxyYztjQgroRhGKyKSba93pOQSYCXK99uPMKj32+r9lxb4tL4v/ej+PiWPgz793K7ba9d1Q3z0SPWsdn//S8YBmag0NUNS1AwLt26QOfO1p+OHaF9ewxXd559awUHkrPZ8NRwXvxtF/OjjzM/Hgis2Lo9uH2zM35fRERERETOdwrEpUEpKrbgYDZVOha5Mt9tOsrsZbHMXhbLj/cMICO3kKERAbVcy9r31bo4nvxpu926L9ceZkyXoCrfm8z8Ik5k5tte3/Tx+rM65/Zj6fR5ebHtdXjqMX4OTsDrnudh48aygjfeCNOn49SyJVRRF2dg8bQhFFsMHB3MdG3uzc/Rx+3K3BzZkvuHtePIyVy6tfA5q7qKiIiIiJzPFIhLg/F51CGe/XknACseuYSwpu6n3ScuNce2PLEkYdjiaUNoG+B5xuctKLJQUGzB06Xufx2OpObQxNk+mDUMg5mL9lYouyY2hfAn5vPXI0Np2dSjwvb4tLwzOuekzk058fN8hu9fx+DUWDzysjFlZlJkdiDP0YU8J2d8czPxy80o28lkgsGD4fnnYejQMzqPyWTC0cF6bRedMrf3x7f05tIOgQA09XQ5o+OJiIiIiFwoFIhLg1EahAMMfn0ZP94zgIvCfKvZA345pZUV4KaP1rHmiWEV1huGwcerD/Hib7u4pncLXru6O8/9vIPPog4DsPLRSwj1O33w/3cUWwzWH0zlSGoOzo5mzGYTH606yNYjaYzsFMC4cnnYDqfkkJxVAMAdA8N5ZHQEby/Zz+xlsQAMeX05/72xF3/tO8FX6+L45JY+DI1oxqj/rKhwXrOlmK5GJqM9cnHes4sbs/bj9PYSTLm5p6+0oyMMG2bNWD5hAgQG/u3r7xnqw3V9w/h6fRwAvVtp+jERERERabwUiEudMwyDHzYfo0tzL77ZcIR1B1Lp37pphXIT313D3pdG4+LoUOlx0nMKOZZWMaA8np7HnoQMOgR5sWBHAi/8upPx3UP4YMUBW5lvNx7lsm4htiAc4Pft8dw9pE0NXGFF7y6L4d+L9lW6beGuJJq2MTG25PW6gykA9Gnly9OXdQLgweHtbYE4wF1fbMJsKSY04wT/eeVL+t3Si2Ex6whNS6R9VhL9C0/QKi0B05E4TIWFFU8aFgaXXw4jR0JQEDd9s5P41Gz8HIqZ3LUZEwZGQPv24FGx5f3vMJlMzJjYlUl9QgnwcsHLtfqkcyIiIiIiFzIF4lLnomJTePi7rXbrdsVnVFr2eFoe4f7WYHDelmOE+rnTq6Sb89ajaZXuY7YUc+NLv/DVlW348NNVNDcMNu3czMVFBfjkZuKVn022syvfzsmgWVYxngU5BGWmsOeNZeRGBZCYmo2fuxNebk7QoYM1WHVzO6drXh2bXO32b2LNPJhTQIC3E+sOpALQL7zsywlHs4md17bgnQffpEPSIdqlxNEm5SguxSVB9hfwUVUHd3KCVq2gTRtr9/KxY6FbN7vx3U+2aM+h5BxGdQ484/H5f0f3UJ9aO7aIiIiIyPlCgbjUud0JmdVu7x7qY5teKzYpi49XHeSLtWUt19ueH4mXq5OtzOU9QiiOPUDQkvlctmclHZMOWQPU2fB9dSf6tZJ1v0GrU9d5eFiD19tug1GjKk1QduBEFt9vOsrUoW1o4upEXmExf+5MYGBbf5p6upCYYU2i1jHYi93lvnQI9nYlPj0PCyZikrLxcnflxy3HAOjX2g+Ki+G77+DVV/HYupXHTjlvvoMTJzx8cHR1IdnkwhHvQMZMGAAREdCuHbRuDSEh1nm5q9EhyIsOQV7VlhERERERkZqhQFzq3OGUbNuyg9lEscWw2/75rX3pPn0hAI//uM02XrrUwMd/5KOwTDJ/XcPLafGM+f4QfrF77MoUm8yku3qS4eKBpSRwNpxdCAwP4XCRE/nH4+lw4hDuhfkYnp4ccPYhoYkfye6+FDo4YmDC08nEmJS9EBdnDYa/+w66dIHbb4frr4eAsuzs//h6CzuPZ/Du8lg6BDUhOauA5Kx8grxcmTulv60L/cxrujPmrZUAzL7+IsZ1C+b6/0WxJjaVbcfS2RmfBYBrYR79FnwDs96G/futJ3F2JqHvQD5xDCPWL5SE0NYcaRJAemHZ+9fExZExL4z62/dGRERERERqnwJxqTVJGXmYzSb8S7JiL92TyJdr49hb0iL+9LiO3HpxON9tPGKbL/va3qF4uzsx545+TP5wnS0Idy4q5JLYDVy5axmXxG7ApbiIPuVP5uBg7XZ99dW86RDOrNhCLGZrK/BjozsQeyKLZy7rhKebE4sX7+fNxdbx2q393Vn68CXE7kxgyhebKlzDykeGEnpoD3z5JXz4IezYAQ8+CA8/bD3f6NHQuTMZuxIwezXDYnZgT7kW/4SMPK7731oKiix4uznRIagJCx8cTGxSFmNK5gQPKHl/Pvt2NQMPbeHtQ9EMObQF59yS4/j5wT//Cffdh5trE759Yxldmnvz2+39KCq20PapP2znyysqPse7JiIiIiIitU2BuNSKY2m5XPLGctycHFjx6CWczC7gtk832pXpEeqDg9lEl+ZlKcNfvKILUDKW2DDofWwXE3csZdyeVXjnl7Wk72saxq7AcJL8mzNlylhrQNzUOqZ6amExnlGH2ZeYSc8wX67vF2Z33kCvsumyDiRbpz8b2TmIQ6+OI7+oGCezmavfX8PmuDQ2xp0ktFcv6NULnnsOvv4aPvkENmyAZcusP8BKIM/RmYO+IcQ0DbX9HPEJJO+EMxleAfRo3xKTyUT7wCa093K0trQfP86IHz/j3mW/0y7liP2bGB5uDfpvvRU8rdOxeQMbnhpOaRu4o4PZbpfCYvveBSIiIiIi0vAoEJca9cvW4/yxPZ7jabnW+bmLLLz8+y5Ss+0zd7s4mukYbB2T3KW5N+9NvogWvu44O1oDS89li9n8zYP4HY4p26l5c95v0Z95nYayJyAcgOEdA5kyubfdsV2dHLhzcOsq62g2l43xLg38y+plbUUP9/dkc1wa8enl5ub28YGpU2HqVE5u343jLz+TsyoKh317aXL4AK5FBXQ8cYiOJw5Vet58L2941BEyM6GgrLv9uJJ/i01mtga3I3vQJQy693oYMKDSsd2nBt8iIiIiInJ+qfFA/Pnnn+eFF16wWxcREcGePdYxvHl5eTz00EPMnTuX/Px8Ro0axbvvvktguTmK4+LimDp1KsuWLcPT05Obb76ZGTNm4Oio7w0auhnzd9sHr1inCivVI9SHjNxCHh/TAQ+XsvtZ2k2b3Fx47DGYNQs/AHd3uPZauPFGGDKEwQlZ7Ft5gD2lCc3Cz34+6hEdAzGbwNnRzA2ntJaXCihpNU8qSbIGsPZACu0DmwBw0ZwDQFfo3hW6WzO1h6Yn0iblKD2z4/lHcBHG7t3E74zBvTAPn7wsXDLS7U/i5ATNmmHp3p2Foe35qt0oHJo25T/X9gT3M5/e67Juwfy2LR4AbzdNCyYiIiIi0tDVSmTbuXNnFi9eXHaScgH0gw8+yO+//853332Ht7c39913HxMnTmT16tUAFBcXM27cOIKCglizZg3x8fHcdNNNODk58corr9RGdaWGFBRZKgTh5bk5OfDj1AF2LdJ2tm2DyZOt47AB/vEPePFF8C7rut4pxIuZ1/bgmcs6sWL/CUZ2Cjrrevp6OLP1uZG4OTlUOVVXQJOSQDzTej1/7kzgrkrGkJeymB047BvCYd8QzB0D4ebemAAjLZf7vt/GDZ18GO1VaM247ulpbV338gKTieLCQvLnz+ejscNxcjr7QHrmNT14elwntsSdpHOI9+l3EBERERGRelUrgbijoyNBQRUDpPT0dD766CO++uorLr30UgA++eQTOnbsyNq1a+nfvz8LFy5k165dLF68mMDAQHr06MGLL77IY489xvPPP4+zs3NtVFlqQEp2Wevxx7f05qIwXy6btYqjJ60ZwwuLLZUH4enp8Pzz8M47UFQEgYHWcdhjxlR5Ll8PZy7v0fxv17WJa/UBb6CXK1DWIj53fdwZH/u1q7vZlpv7uPHlHf3+Rg3PnLOjmSBv17JeBSIiIiIi0qDVSiC+f/9+QkJCcHV1JTIykhkzZhAWFsamTZsoLCxk+PDhtrIdOnQgLCyMqKgo+vfvT1RUFF27drXrqj5q1CimTp3Kzp076dmzZ6XnzM/PJz+/LBDMyLDO1VxYWEhhYWGl+zQEpXVryHU8E9uPpfP9Zmt38YAmLgxqY+0y/sL4jtz++WYAujb3qnCdpgULcLjnHkxHrd3XLePHU/zee9apwerxPfFzs47N3nj4JENeW4azYxWt+MC8qf3pHOJFanYBLo5mPJxNZ3U/L5RnQP4e3f/GTfdf9Aw0brr/omfgwnOm97LGA/F+/frx6aefEhERQXx8PC+88AKDBg1ix44dJCQk4OzsjI+Pj90+gYGBJCQkAJCQkGAXhJduL91WlRkzZlQYmw6wcOFC3N3dz/Gqat+iRYvquwrn5JVoBxJzrcGqY3Ee8+fPt22b1NrExmQz4/1TbOudsrLo8tFHhJVkHc8KCmLbXXdxomdP2Lix4gnqWGo+lP56HE7Nqbbsno2rOFwDv0nn+zMg50b3v3HT/Rc9A42b7r/oGbhw5ORUHzuUqvFAfEy57sTdunWjX79+tGzZkm+//RY3N7eaPp3NE088wbRp02yvMzIyCA0NZeTIkXh5edXaec9VYWEhixYtYsSIEX9rfHB9ScrMJ7ewmHUHUnnq511228IC/Rg7tmyW77HlNxoGpm++weGxxzDFx2OYTFjuvx+XF16gTwP6wqSw2MILmxdXWH9Dv1DWxKYybXhbth/LoGeoN8M6Bpzbuc7TZ0Bqhu5/46b7L3oGGjfdf9EzcOEp7Zl9OrWehtzHx4f27dsTExPDiBEjKCgoIC0tza5VPDEx0TamPCgoiPXr19sdIzEx0batKi4uLri4uFRY7+TkdF481OdLPUtd8d5fnMjMr3TbZd2bV34tO3bAPffAypXW1xERmD7+GIcBA6g4SVf9qupWjOkawktXWseAX9ajps95fj0DUrN0/xs33X/RM9C46f6LnoELx5nex1qfkDgrK4vY2FiCg4Pp1asXTk5OLFmyxLZ97969xMXFERkZCUBkZCTbt28nKSnJVmbRokV4eXnRqVOn2q6unIHs/KIqg/Ab+ocx+dQpwQoKYPp0uOgiaxDu5mbNhh4dbZ0ru4Ha8syICusigprUQ01ERERERORCUuMt4g8//DDjx4+nZcuWHD9+nOeeew4HBweuu+46vL29uf3225k2bRp+fn54eXnxj3/8g8jISPr37w/AyJEj6dSpEzfeeCOvvfYaCQkJPP3009x7772VtnhL3TIMg87P/Wm3zsfdiWkj2jO0fQBhTU/pXr55M9x6q3VqMoDx463Z0cMqn7+7IfH1cGb145dy8atLAfj6zv74e+oZFBERERGRc1PjgfjRo0e57rrrSElJoVmzZgwcOJC1a9fSrFkzAN58803MZjNXXXUV+fn5jBo1infffde2v4ODA7/99htTp04lMjISDw8Pbr75ZqZPn17TVZW/IfZEtt3rcV2DmTK4Nd1DfewL5uVZW8Ffew2Ki6FpU5g1CyZNss6lfZ5o7uPG8+M7seN4Bn3D/eq7OiIiIiIicgGo8UB87ty51W53dXVl9uzZzJ49u8oyLVu2tMu6LXUrPaeQ7tMXAjBtRHsOJmezOz6Da/uE8q8Fe2zlfvvHQLo09654gKgouO022FNS9pprrEF4wLklNasvt1wcXt9VEBERERGRC0itJ2uT88v87fE89sM22+uZi/bZll/4tSw7+qsTu1YMwhMSrK3g778PhgGBgfDee3DllbVebxERERERkfOFAvHzTFJmHo9+v41+4U25tk8ofh7ONXLc42m5DCgZC306fVr5MqlvuTHeGRnwxhswcyZkl3Rdv+kmePNN8FN3bhERERERkfIUiJ9nXvptN8v3nmD53hO8/1csCx4YxJ87Eog6kMLT4zoR6vf35uL+59wtFdaN6hyIh7MjfcL9OHoyh9nLYgG4Z2hba4Hdu+G//4XPPoO0NOu6vn3h1Vfhkkv+Vj1EREREREQudArEG4i8wmJmLT/IyE6BlY+7LrEmNsW2nJ5bSOSMslbshIx8fr734r91/g2HTtqW37+hFwt3JvDMZZ3wLW1xz87mFo90jq3dSvdvN8GCBdax4KUiIuDll2HixPMqGZuIiIiIiEhdUyDeAFgM6DrdOrf67GUxxL4yttJym+NOkpxlnb97ZKdAFu5KtNu+9UganZ9dwJ8PDqaFb+Ut4zFJWXyzIY7/rTzIHQPDefqyTjz5/Va88rLwysti7ujmtIj6ldF798LXMRAXB0eOQHIyzYBm5Q9mNlunI7v7bhg50vpaREREREREqqVAvAFIyC1bLrYYRMWmENmmKQDJWflsPHSSL9YeYnWMtTV8fPcQHh/ToUIgDpBdUMzAfy0DoEeoD+/f0Iugn+aS8/FnZCSlkJ2ex3gMJhYX4fbffPItubyYmcErhsV6gP9WU9GmTa0t3+3bw0UXwdVXQ3BwjbwHIiIiIiIijYUC8Xp295wtLNvrYLfuuv+txdvNiev6hvH+X7EV9rljYDjNfdz411VdKbbAdX1DOZaWawvAS0UfSeP+uVt4beduWq1cjjsQVE1dCpxdcW4ebA22IyKgXTto1QrCwiA0FHx8zvVyRUREREREGj0F4vVsyZ4TgHVMdfcW3mw9mg5Yx39XFoQ/N74T3UN9ALi2T1nm8ha+7ux9aTQ3frie9YdSbevXH0zl7qxQ2o9/mGxnNywmMwYwaUBrPtyUQJprE9JdPfnkgZF0aRtYa9cpIiIiIiIiVgrE69mQ9v78tS8ZgJ5hvky8qAXP/bLTrsxbk3pwWbcQHMzVJ0FzcXTg27sjWbQrkRl/7ObACetUYnsCwtkTEG4r9/rV3RjdO5SF30azcfMxADq3CajJyxIREREREZEqKBCvZ89d1oFLZ64CwN/TmZsHtGJYxwCe/GkHK/ad4Os7+9vGi5+pEZ0CGdEpkG82xPHYD9tt66OfHcHWo+kMbucPwPMTOnNxG3/Gdw/BpEznIiIiIiIidUKBeD0L9XWnhYdBUr4D47qFANZu5p/f1vecj31tnzB2x2fy6ZpDPDa6Az7uzgxpX5b33MvViat6tTjn84iIiIiIiMiZUyDeAEztWEy/gYMI9/eo8WM/e1knruzZnE4hXjV+bBERERERETl7CsQbAE8naNm08nm/z5XZbLIldxMREREREZH6Z67vCoiIiIiIiIg0JgrERUREREREROqQAnERERERERGROqRAXERERERERKQOKRAXERERERERqUMKxEVERERERETqkAJxERERERERkTqkQFxERERERESkDikQFxEREREREalDCsRFRERERERE6pACcREREREREZE6pEBcREREREREpA4pEBcRERERERGpQwrERUREREREROqQAnERERERERGROqRAXERERERERKQOKRAXERERERERqUOO9V2B2mIYBgAZGRn1XJPqFRYWkpOTQ0ZGBk5OTvVdHakHegYaN93/xk33X/QMNG66/6Jn4MJTGn+WxqNVuWAD8czMTABCQ0PruSYiIiIiIiLSmGRmZuLt7V3ldpNxulD9PGWxWDh+/DhNmjTBZDLVd3WqlJGRQWhoKEeOHMHLy6u+qyP1QM9A46b737jp/ouegcZN91/0DFx4DMMgMzOTkJAQzOaqR4JfsC3iZrOZFi1a1Hc1zpiXl5d++Ro5PQONm+5/46b7L3oGGjfdf9EzcGGpriW8lJK1iYiIiIiIiNQhBeIiIiIiIiIidUiBeD1zcXHhueeew8XFpb6rIvVEz0DjpvvfuOn+i56Bxk33X/QMNF4XbLI2ERERERERkYZILeIiIiIiIiIidUiBuIiIiIiIiEgdUiAuIiIiIiIiUocUiIuIiIiIiIjUIQXi9Wz27Nm0atUKV1dX+vXrx/r16+u7SnKOnn/+eUwmk91Phw4dbNvz8vK49957adq0KZ6enlx11VUkJibaHSMuLo5x48bh7u5OQEAAjzzyCEVFRXV9KXKGVqxYwfjx4wkJCcFkMjFv3jy77YZh8OyzzxIcHIybmxvDhw9n//79dmVSU1OZPHkyXl5e+Pj4cPvtt5OVlWVXZtu2bQwaNAhXV1dCQ0N57bXXavvS5Ayc7v7fcsstFT4TRo8ebVdG9//8NWPGDPr06UOTJk0ICAjgiiuuYO/evXZlaupzf/ny5Vx00UW4uLjQtm1bPv3009q+PDkDZ/IMDB06tMLnwN13321XRs/A+em9996jW7dueHl54eXlRWRkJH/88Ydtu37/pUqG1Ju5c+cazs7Oxscff2zs3LnTuPPOOw0fHx8jMTGxvqsm5+C5554zOnfubMTHx9t+Tpw4Ydt+9913G6GhocaSJUuMjRs3Gv379zcGDBhg215UVGR06dLFGD58uLFlyxZj/vz5hr+/v/HEE0/Ux+XIGZg/f77x1FNPGT/++KMBGD/99JPd9ldffdXw9vY25s2bZ2zdutWYMGGCER4ebuTm5trKjB492ujevbuxdu1aY+XKlUbbtm2N6667zrY9PT3dCAwMNCZPnmzs2LHD+Prrrw03Nzfjv//9b11dplThdPf/5ptvNkaPHm33mZCammpXRvf//DVq1Cjjk08+MXbs2GFER0cbY8eONcLCwoysrCxbmZr43D9w4IDh7u5uTJs2zdi1a5cxa9Ysw8HBwViwYEGdXq9UdCbPwJAhQ4w777zT7nMgPT3dtl3PwPnrl19+MX7//Xdj3759xt69e40nn3zScHJyMnbs2GEYhn7/pWoKxOtR3759jXvvvdf2uri42AgJCTFmzJhRj7WSc/Xcc88Z3bt3r3RbWlqa4eTkZHz33Xe2dbt37zYAIyoqyjAM6x/1ZrPZSEhIsJV57733DC8vLyM/P79W6y7n7tRAzGKxGEFBQcbrr79uW5eWlma4uLgYX3/9tWEYhrFr1y4DMDZs2GAr88cffxgmk8k4duyYYRiG8e677xq+vr52z8Bjjz1mRERE1PIVydmoKhC//PLLq9xH9//CkpSUZADGX3/9ZRhGzX3uP/roo0bnzp3tznXttdcao0aNqu1LkrN06jNgGNZA/J///GeV++gZuLD4+voaH374oX7/pVrqml5PCgoK2LRpE8OHD7etM5vNDB8+nKioqHqsmdSE/fv3ExISQuvWrZk8eTJxcXEAbNq0icLCQrv73qFDB8LCwmz3PSoqiq5duxIYGGgrM2rUKDIyMti5c2fdXoics4MHD5KQkGB3z729venXr5/dPffx8aF37962MsOHD8dsNrNu3TpbmcGDB+Ps7GwrM2rUKPbu3cvJkyfr6Grk71q+fDkBAQFEREQwdepUUlJSbNt0/y8s6enpAPj5+QE197kfFRVld4zSMvqboeE59RkoNWfOHPz9/enSpQtPPPEEOTk5tm16Bi4MxcXFzJ07l+zsbCIjI/X7L9VyrO8KNFbJyckUFxfb/dIBBAYGsmfPnnqqldSEfv368emnnxIREUF8fDwvvPACgwYNYseOHSQkJODs7IyPj4/dPoGBgSQkJACQkJBQ6XNRuk3OL6X3rLJ7Wv6eBwQE2G13dHTEz8/Prkx4eHiFY5Ru8/X1rZX6y7kbPXo0EydOJDw8nNjYWJ588knGjBlDVFQUDg4Ouv8XEIvFwgMPPMDFF19Mly5dAGrsc7+qMhkZGeTm5uLm5lYblyRnqbJnAOD666+nZcuWhISEsG3bNh577DH27t3Ljz/+COgZON9t376dyMhI8vLy8PT05KeffqJTp05ER0fr91+qpEBcpIaNGTPGttytWzf69etHy5Yt+fbbb/VBKdIITZo0ybbctWtXunXrRps2bVi+fDnDhg2rx5pJTbv33nvZsWMHq1atqu+qSD2p6hmYMmWKbblr164EBwczbNgwYmNjadOmTV1XU2pYREQE0dHRpKen8/3333PzzTfz119/1Xe1pIFT1/R64u/vj4ODQ4WsiYmJiQQFBdVTraQ2+Pj40L59e2JiYggKCqKgoIC0tDS7MuXve1BQUKXPRek2Ob+U3rPqfteDgoJISkqy215UVERqaqqeiwtQ69at8ff3JyYmBtD9v1Dcd999/PbbbyxbtowWLVrY1tfU535VZby8vPQlbwNR1TNQmX79+gHYfQ7oGTh/OTs707ZtW3r16sWMGTPo3r07b731ln7/pVoKxOuJs7MzvXr1YsmSJbZ1FouFJUuWEBkZWY81k5qWlZVFbGwswcHB9OrVCycnJ7v7vnfvXuLi4mz3PTIyku3bt9v9Yb5o0SK8vLzo1KlTnddfzk14eDhBQUF29zwjI4N169bZ3fO0tDQ2bdpkK7N06VIsFovtj7XIyEhWrFhBYWGhrcyiRYuIiIhQt+TzzNGjR0lJSSE4OBjQ/T/fGYbBfffdx08//cTSpUsrDCGoqc/9yMhIu2OUltHfDPXvdM9AZaKjowHsPgf0DFw4LBYL+fn5+v2X6tV3trjGbO7cuYaLi4vx6aefGrt27TKmTJli+Pj42GVNlPPPQw89ZCxfvtw4ePCgsXr1amP48OGGv7+/kZSUZBiGdRqLsLAwY+nSpcbGjRuNyMhIIzIy0rZ/6TQWI0eONKKjo40FCxYYzZo10/RlDVhmZqaxZcsWY8uWLQZgzJw509iyZYtx+PBhwzCs05f5+PgYP//8s7Ft2zbj8ssvr3T6sp49exrr1q0zVq1aZbRr185u+qq0tDQjMDDQuPHGG40dO3YYc+fONdzd3TV9VQNQ3f3PzMw0Hn74YSMqKso4ePCgsXjxYuOiiy4y2rVrZ+Tl5dmOoft//po6darh7e1tLF++3G5qqpycHFuZmvjcL52+6JFHHjF2795tzJ49W9MXNRCnewZiYmKM6dOnGxs3bjQOHjxo/Pzzz0br1q2NwYMH246hZ+D89fjjjxt//fWXcfDgQWPbtm3G448/bphMJmPhwoWGYej3X6qmQLyezZo1ywgLCzOcnZ2Nvn37GmvXrq3vKsk5uvbaa43g4GDD2dnZaN68uXHttdcaMTExtu25ubnGPffcY/j6+hru7u7GlVdeacTHx9sd49ChQ8aYMWMMNzc3w9/f33jooYeMwsLCur4UOUPLli0zgAo/N998s2EY1inMnnnmGSMwMNBwcXExhg0bZuzdu9fuGCkpKcZ1111neHp6Gl5eXsatt95qZGZm2pXZunWrMXDgQMPFxcVo3ry58eqrr9bVJUo1qrv/OTk5xsiRI41mzZoZTk5ORsuWLY0777yzwheuuv/nr8ruPWB88skntjI19bm/bNkyo0ePHoazs7PRunVru3NI/TndMxAXF2cMHjzY8PPzM1xcXIy2bdsajzzyiN084oahZ+B8ddtttxktW7Y0nJ2djWbNmhnDhg2zBeGGod9/qZrJMAyj7trfRURERERERBo3jREXERERERERqUMKxEVERERERETqkAJxERERERERkTqkQFxERERERESkDikQFxEREREREalDCsRFRERERERE6pACcREREREREZE6pEBcREREREREpA4pEBcRERERERGpQwrERUREREREROqQAnERERERERGROqRAXERERERERKQO/T+SBJEdidRZHQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (12,6))\n",
        "plt.plot(df.Close)\n",
        "plt.plot(ma100, 'r')\n",
        "plt.grid(True)\n",
        "plt.title('Graph Of Moving Averages Of 100 Days')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining 200 days moving averages and plotting comparision graph with 100 days moving averages\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "LQKvGsXwmN65"
      },
      "id": "LQKvGsXwmN65"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "46c89811",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "46c89811",
        "outputId": "a7a974f0-937e-4265-b375-929053f46272"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0               NaN\n",
              "1               NaN\n",
              "2               NaN\n",
              "3               NaN\n",
              "4               NaN\n",
              "           ...     \n",
              "3288    3253.645248\n",
              "3289    3253.217498\n",
              "3290    3253.751997\n",
              "3291    3254.481997\n",
              "3292    3255.541248\n",
              "Name: Close, Length: 3293, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "ma200 = df.Close.rolling(200).mean()\n",
        "ma200"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "d7c0ce2e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "d7c0ce2e",
        "outputId": "853bd4ca-af7d-4c81-9d9d-92ec6eddbef2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Comparision Of 100 Days And 200 Days Moving Averages')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (12,6))\n",
        "plt.plot(df.Close)\n",
        "plt.plot(ma100, 'r')\n",
        "plt.plot(ma200, 'g')\n",
        "plt.grid(True)\n",
        "plt.title('Comparision Of 100 Days And 200 Days Moving Averages')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "45a099a7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45a099a7",
        "outputId": "c3685930-176a-4c2d-b391-69866c4637c5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3293, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Spliting the dataset into training (70%) and testing (30%) set"
      ],
      "metadata": {
        "id": "bCw5fsrlmfpm"
      },
      "id": "bCw5fsrlmfpm"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "9610c5fc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9610c5fc",
        "outputId": "56ee8fcd-ce88-4705-f511-b030a00712c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2305, 7)\n",
            "(988, 7)\n"
          ]
        }
      ],
      "source": [
        "# Splitting data into training and testing\n",
        "\n",
        "train = pd.DataFrame(data[0:int(len(data)*0.70)])\n",
        "test = pd.DataFrame(data[int(len(data)*0.70): int(len(data))])\n",
        "\n",
        "print(train.shape)\n",
        "print(test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "2cb33dc0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2cb33dc0",
        "outputId": "bccf505c-0272-48e4-8b35-138abd6723dd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date        Open        High         Low       Close   Adj Close  \\\n",
              "0 2010-01-04  377.399994  379.450012  374.524994  375.825012  289.802155   \n",
              "1 2010-01-05  377.500000  379.774994  373.274994  375.924988  289.879333   \n",
              "2 2010-01-06  375.924988  376.100006  366.500000  367.424988  283.325012   \n",
              "3 2010-01-07  367.750000  369.700012  355.500000  357.200012  275.440186   \n",
              "4 2010-01-08  358.000000  359.250000  348.250000  349.899994  269.811127   \n",
              "\n",
              "    Volume  \n",
              "0  1963682  \n",
              "1  2014488  \n",
              "2  3349176  \n",
              "3  6474892  \n",
              "4  6048178  "
            ],
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              "      <th>Date</th>\n",
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              "    <tr>\n",
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              "      <td>2010-01-05</td>\n",
              "      <td>377.500000</td>\n",
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              "      <td>2010-01-06</td>\n",
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              "      <td>2010-01-08</td>\n",
              "      <td>358.000000</td>\n",
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              "          document.querySelector('#df-702ff593-b614-432a-ba32-0caaa91a49ed button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-702ff593-b614-432a-ba32-0caaa91a49ed');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "91f5ef8b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "91f5ef8b",
        "outputId": "8e92506b-b4ea-4eec-c643-0f6e2d34bb90"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date         Open         High          Low        Close  \\\n",
              "2305 2019-05-13  2133.000000  2167.000000  2121.649902  2128.750000   \n",
              "2306 2019-05-14  2135.000000  2159.800049  2072.350098  2092.350098   \n",
              "2307 2019-05-15  2097.000000  2124.000000  2084.500000  2095.399902   \n",
              "2308 2019-05-16  2096.000000  2124.000000  2077.050049  2108.750000   \n",
              "2309 2019-05-17  2112.600098  2135.000000  2090.000000  2095.449951   \n",
              "\n",
              "        Adj Close   Volume  \n",
              "2305  1938.281494  1701744  \n",
              "2306  1905.138428  2456110  \n",
              "2307  1907.915405  2346876  \n",
              "2308  1920.071045  1946529  \n",
              "2309  1907.960938  1947908  "
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2305</th>\n",
              "      <td>2019-05-13</td>\n",
              "      <td>2133.000000</td>\n",
              "      <td>2167.000000</td>\n",
              "      <td>2121.649902</td>\n",
              "      <td>2128.750000</td>\n",
              "      <td>1938.281494</td>\n",
              "      <td>1701744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2306</th>\n",
              "      <td>2019-05-14</td>\n",
              "      <td>2135.000000</td>\n",
              "      <td>2159.800049</td>\n",
              "      <td>2072.350098</td>\n",
              "      <td>2092.350098</td>\n",
              "      <td>1905.138428</td>\n",
              "      <td>2456110</td>\n",
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              "    <tr>\n",
              "      <th>2307</th>\n",
              "      <td>2019-05-15</td>\n",
              "      <td>2097.000000</td>\n",
              "      <td>2124.000000</td>\n",
              "      <td>2084.500000</td>\n",
              "      <td>2095.399902</td>\n",
              "      <td>1907.915405</td>\n",
              "      <td>2346876</td>\n",
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              "    <tr>\n",
              "      <th>2308</th>\n",
              "      <td>2019-05-16</td>\n",
              "      <td>2096.000000</td>\n",
              "      <td>2124.000000</td>\n",
              "      <td>2077.050049</td>\n",
              "      <td>2108.750000</td>\n",
              "      <td>1920.071045</td>\n",
              "      <td>1946529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2309</th>\n",
              "      <td>2019-05-17</td>\n",
              "      <td>2112.600098</td>\n",
              "      <td>2135.000000</td>\n",
              "      <td>2090.000000</td>\n",
              "      <td>2095.449951</td>\n",
              "      <td>1907.960938</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7619bf43-c0a8-4d13-a7c1-638955f93fd0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7619bf43-c0a8-4d13-a7c1-638955f93fd0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "test.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Using MinMax scaler for normalization of the dataset\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "-a818gdwm_WV"
      },
      "id": "-a818gdwm_WV"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "01f04df7",
      "metadata": {
        "id": "01f04df7"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(0,1))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_close = train.iloc[:, 4:5].values\n",
        "test_close = test.iloc[:, 4:5].values"
      ],
      "metadata": {
        "id": "53oSB51dB6Tw"
      },
      "id": "53oSB51dB6Tw",
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "d97e7ff8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d97e7ff8",
        "outputId": "eae5e982-ee9f-4be5-af17-b78fc6e6dd45"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.01363465],\n",
              "       [0.01368698],\n",
              "       [0.00923805],\n",
              "       ...,\n",
              "       [0.94373422],\n",
              "       [0.95404523],\n",
              "       [0.93481018]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "data_training_array = scaler.fit_transform(train_close)\n",
        "data_training_array"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "10227978",
      "metadata": {
        "id": "10227978"
      },
      "outputs": [],
      "source": [
        "x_train = []\n",
        "y_train = [] \n",
        "\n",
        "for i in range(100, data_training_array.shape[0]):\n",
        "    x_train.append(data_training_array[i-100: i])\n",
        "    y_train.append(data_training_array[i, 0])\n",
        "\n",
        "x_train, y_train = np.array(x_train), np.array(y_train) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "0ea6446e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ea6446e",
        "outputId": "16c4a903-7a88-43c7-e30b-89149f057b75"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2205, 100, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "x_train.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ML Model (LSTM)\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "dsMIlQ8DnVOJ"
      },
      "id": "dsMIlQ8DnVOJ"
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "e60ed317",
      "metadata": {
        "id": "e60ed317"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.layers import Dense, Dropout, LSTM\n",
        "from tensorflow.keras.models import Sequential"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "17082610",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "17082610",
        "outputId": "e3a8a228-487d-40be-a6ce-7e999581b9d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_2 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_3 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        }
      ],
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(units = 50, activation = 'relu', return_sequences=True\n",
        "              ,input_shape = (x_train.shape[1], 1)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "\n",
        "model.add(LSTM(units = 60, activation = 'relu', return_sequences=True))\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "\n",
        "model.add(LSTM(units = 80, activation = 'relu', return_sequences=True))\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "\n",
        "model.add(LSTM(units = 120, activation = 'relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(units = 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "5f5a1db3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5f5a1db3",
        "outputId": "23244ace-c6af-46ed-e068-77c1cd563dd0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 100, 50)           10400     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 100, 50)           0         \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 100, 60)           26640     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 100, 60)           0         \n",
            "                                                                 \n",
            " lstm_2 (LSTM)               (None, 100, 80)           45120     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 100, 80)           0         \n",
            "                                                                 \n",
            " lstm_3 (LSTM)               (None, 120)               96480     \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 120)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 121       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,761\n",
            "Trainable params: 178,761\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training the model\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "KgEnUfCnn_1r"
      },
      "id": "KgEnUfCnn_1r"
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "1d0c9b64",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1d0c9b64",
        "outputId": "8014670b-4fcf-4fe9-d60a-786b45c8b88c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "69/69 [==============================] - 66s 727ms/step - loss: 0.0260 - mean_absolute_error: 0.1091\n",
            "Epoch 2/100\n",
            "69/69 [==============================] - 47s 681ms/step - loss: 0.0071 - mean_absolute_error: 0.0577\n",
            "Epoch 3/100\n",
            "69/69 [==============================] - 48s 696ms/step - loss: 0.0054 - mean_absolute_error: 0.0522\n",
            "Epoch 4/100\n",
            "69/69 [==============================] - 49s 716ms/step - loss: 0.0050 - mean_absolute_error: 0.0503\n",
            "Epoch 5/100\n",
            "69/69 [==============================] - 47s 681ms/step - loss: 0.0046 - mean_absolute_error: 0.0480\n",
            "Epoch 6/100\n",
            "69/69 [==============================] - 46s 663ms/step - loss: 0.0045 - mean_absolute_error: 0.0465\n",
            "Epoch 7/100\n",
            "69/69 [==============================] - 47s 687ms/step - loss: 0.0040 - mean_absolute_error: 0.0443\n",
            "Epoch 8/100\n",
            "69/69 [==============================] - 47s 683ms/step - loss: 0.0050 - mean_absolute_error: 0.0486\n",
            "Epoch 9/100\n",
            "69/69 [==============================] - 47s 681ms/step - loss: 0.0044 - mean_absolute_error: 0.0464\n",
            "Epoch 10/100\n",
            "69/69 [==============================] - 45s 659ms/step - loss: 0.0037 - mean_absolute_error: 0.0433\n",
            "Epoch 11/100\n",
            "69/69 [==============================] - 47s 676ms/step - loss: 0.0036 - mean_absolute_error: 0.0418\n",
            "Epoch 12/100\n",
            "69/69 [==============================] - 47s 676ms/step - loss: 0.0033 - mean_absolute_error: 0.0406\n",
            "Epoch 13/100\n",
            "69/69 [==============================] - 47s 684ms/step - loss: 0.0033 - mean_absolute_error: 0.0400\n",
            "Epoch 14/100\n",
            "69/69 [==============================] - 46s 668ms/step - loss: 0.0031 - mean_absolute_error: 0.0403\n",
            "Epoch 15/100\n",
            "69/69 [==============================] - 46s 659ms/step - loss: 0.0032 - mean_absolute_error: 0.0390\n",
            "Epoch 16/100\n",
            "69/69 [==============================] - 47s 676ms/step - loss: 0.0030 - mean_absolute_error: 0.0379\n",
            "Epoch 17/100\n",
            "69/69 [==============================] - 47s 678ms/step - loss: 0.0026 - mean_absolute_error: 0.0357\n",
            "Epoch 18/100\n",
            "69/69 [==============================] - 47s 677ms/step - loss: 0.0029 - mean_absolute_error: 0.0381\n",
            "Epoch 19/100\n",
            "69/69 [==============================] - 46s 657ms/step - loss: 0.0031 - mean_absolute_error: 0.0391\n",
            "Epoch 20/100\n",
            "69/69 [==============================] - 47s 683ms/step - loss: 0.0028 - mean_absolute_error: 0.0371\n",
            "Epoch 21/100\n",
            "69/69 [==============================] - 47s 675ms/step - loss: 0.0031 - mean_absolute_error: 0.0393\n",
            "Epoch 22/100\n",
            "69/69 [==============================] - 47s 680ms/step - loss: 0.0029 - mean_absolute_error: 0.0372\n",
            "Epoch 23/100\n",
            "69/69 [==============================] - 45s 658ms/step - loss: 0.0025 - mean_absolute_error: 0.0362\n",
            "Epoch 24/100\n",
            "69/69 [==============================] - 47s 676ms/step - loss: 0.0025 - mean_absolute_error: 0.0359\n",
            "Epoch 25/100\n",
            "69/69 [==============================] - 47s 679ms/step - loss: 0.0023 - mean_absolute_error: 0.0353\n",
            "Epoch 26/100\n",
            "69/69 [==============================] - 46s 668ms/step - loss: 0.0023 - mean_absolute_error: 0.0344\n",
            "Epoch 27/100\n",
            "69/69 [==============================] - 46s 660ms/step - loss: 0.0023 - mean_absolute_error: 0.0345\n",
            "Epoch 28/100\n",
            "69/69 [==============================] - 46s 675ms/step - loss: 0.0024 - mean_absolute_error: 0.0349\n",
            "Epoch 29/100\n",
            "69/69 [==============================] - 47s 677ms/step - loss: 0.0023 - mean_absolute_error: 0.0343\n",
            "Epoch 30/100\n",
            "69/69 [==============================] - 46s 666ms/step - loss: 0.0022 - mean_absolute_error: 0.0339\n",
            "Epoch 31/100\n",
            "69/69 [==============================] - 47s 660ms/step - loss: 0.0025 - mean_absolute_error: 0.0359\n",
            "Epoch 32/100\n",
            "69/69 [==============================] - 47s 684ms/step - loss: 0.0022 - mean_absolute_error: 0.0345\n",
            "Epoch 33/100\n",
            "69/69 [==============================] - 47s 683ms/step - loss: 0.0022 - mean_absolute_error: 0.0336\n",
            "Epoch 34/100\n",
            "69/69 [==============================] - 47s 680ms/step - loss: 0.0023 - mean_absolute_error: 0.0351\n",
            "Epoch 35/100\n",
            "69/69 [==============================] - 45s 655ms/step - loss: 0.0020 - mean_absolute_error: 0.0327\n",
            "Epoch 36/100\n",
            "69/69 [==============================] - 47s 680ms/step - loss: 0.0019 - mean_absolute_error: 0.0318\n",
            "Epoch 37/100\n",
            "69/69 [==============================] - 47s 680ms/step - loss: 0.0019 - mean_absolute_error: 0.0319\n",
            "Epoch 38/100\n",
            "69/69 [==============================] - 45s 657ms/step - loss: 0.0021 - mean_absolute_error: 0.0334\n",
            "Epoch 39/100\n",
            "69/69 [==============================] - 46s 658ms/step - loss: 0.0020 - mean_absolute_error: 0.0327\n",
            "Epoch 40/100\n",
            "69/69 [==============================] - 47s 684ms/step - loss: 0.0021 - mean_absolute_error: 0.0335\n",
            "Epoch 41/100\n",
            "69/69 [==============================] - 47s 679ms/step - loss: 0.0022 - mean_absolute_error: 0.0338\n",
            "Epoch 42/100\n",
            "69/69 [==============================] - 45s 654ms/step - loss: 0.0021 - mean_absolute_error: 0.0329\n",
            "Epoch 43/100\n",
            "69/69 [==============================] - 47s 675ms/step - loss: 0.0020 - mean_absolute_error: 0.0332\n",
            "Epoch 44/100\n",
            "69/69 [==============================] - 47s 676ms/step - loss: 0.0019 - mean_absolute_error: 0.0323\n",
            "Epoch 45/100\n",
            "69/69 [==============================] - 47s 680ms/step - loss: 0.0018 - mean_absolute_error: 0.0318\n",
            "Epoch 46/100\n",
            "69/69 [==============================] - 45s 656ms/step - loss: 0.0018 - mean_absolute_error: 0.0316\n",
            "Epoch 47/100\n",
            "69/69 [==============================] - 47s 663ms/step - loss: 0.0019 - mean_absolute_error: 0.0322\n",
            "Epoch 48/100\n",
            "69/69 [==============================] - 47s 680ms/step - loss: 0.0018 - mean_absolute_error: 0.0317\n",
            "Epoch 49/100\n",
            "69/69 [==============================] - 47s 679ms/step - loss: 0.0018 - mean_absolute_error: 0.0312\n",
            "Epoch 50/100\n",
            "69/69 [==============================] - 45s 656ms/step - loss: 0.0018 - mean_absolute_error: 0.0317\n",
            "Epoch 51/100\n",
            "69/69 [==============================] - 46s 665ms/step - loss: 0.0020 - mean_absolute_error: 0.0323\n",
            "Epoch 52/100\n",
            "69/69 [==============================] - 46s 675ms/step - loss: 0.0018 - mean_absolute_error: 0.0319\n",
            "Epoch 53/100\n",
            "69/69 [==============================] - 47s 677ms/step - loss: 0.0018 - mean_absolute_error: 0.0309\n",
            "Epoch 54/100\n",
            "69/69 [==============================] - 45s 656ms/step - loss: 0.0018 - mean_absolute_error: 0.0313\n",
            "Epoch 55/100\n",
            "69/69 [==============================] - 46s 675ms/step - loss: 0.0018 - mean_absolute_error: 0.0318\n",
            "Epoch 56/100\n",
            "69/69 [==============================] - 46s 672ms/step - loss: 0.0019 - mean_absolute_error: 0.0320\n",
            "Epoch 57/100\n",
            "69/69 [==============================] - 46s 664ms/step - loss: 0.0019 - mean_absolute_error: 0.0324\n",
            "Epoch 58/100\n",
            "69/69 [==============================] - 45s 652ms/step - loss: 0.0018 - mean_absolute_error: 0.0318\n",
            "Epoch 59/100\n",
            "69/69 [==============================] - 46s 674ms/step - loss: 0.0018 - mean_absolute_error: 0.0317\n",
            "Epoch 60/100\n",
            "69/69 [==============================] - 47s 682ms/step - loss: 0.0018 - mean_absolute_error: 0.0313\n",
            "Epoch 61/100\n",
            "69/69 [==============================] - 45s 648ms/step - loss: 0.0018 - mean_absolute_error: 0.0309\n",
            "Epoch 62/100\n",
            "69/69 [==============================] - 46s 663ms/step - loss: 0.0017 - mean_absolute_error: 0.0305\n",
            "Epoch 63/100\n",
            "69/69 [==============================] - 46s 671ms/step - loss: 0.0017 - mean_absolute_error: 0.0304\n",
            "Epoch 64/100\n",
            "69/69 [==============================] - 46s 673ms/step - loss: 0.0018 - mean_absolute_error: 0.0305\n",
            "Epoch 65/100\n",
            "69/69 [==============================] - 45s 650ms/step - loss: 0.0017 - mean_absolute_error: 0.0299\n",
            "Epoch 66/100\n",
            "69/69 [==============================] - 46s 669ms/step - loss: 0.0018 - mean_absolute_error: 0.0313\n",
            "Epoch 67/100\n",
            "69/69 [==============================] - 46s 674ms/step - loss: 0.0018 - mean_absolute_error: 0.0315\n",
            "Epoch 68/100\n",
            "69/69 [==============================] - 45s 650ms/step - loss: 0.0018 - mean_absolute_error: 0.0312\n",
            "Epoch 69/100\n",
            "69/69 [==============================] - 47s 677ms/step - loss: 0.0018 - mean_absolute_error: 0.0310\n",
            "Epoch 70/100\n",
            "69/69 [==============================] - 46s 675ms/step - loss: 0.0018 - mean_absolute_error: 0.0309\n",
            "Epoch 71/100\n",
            "69/69 [==============================] - 47s 676ms/step - loss: 0.0019 - mean_absolute_error: 0.0319\n",
            "Epoch 72/100\n",
            "69/69 [==============================] - 45s 657ms/step - loss: 0.0019 - mean_absolute_error: 0.0321\n",
            "Epoch 73/100\n",
            "69/69 [==============================] - 46s 669ms/step - loss: 0.0018 - mean_absolute_error: 0.0313\n",
            "Epoch 74/100\n",
            "69/69 [==============================] - 47s 678ms/step - loss: 0.0018 - mean_absolute_error: 0.0313\n",
            "Epoch 75/100\n",
            "69/69 [==============================] - 47s 679ms/step - loss: 0.0017 - mean_absolute_error: 0.0301\n",
            "Epoch 76/100\n",
            "69/69 [==============================] - 45s 655ms/step - loss: 0.0017 - mean_absolute_error: 0.0307\n",
            "Epoch 77/100\n",
            "69/69 [==============================] - 46s 672ms/step - loss: 0.0017 - mean_absolute_error: 0.0303\n",
            "Epoch 78/100\n",
            "69/69 [==============================] - 46s 675ms/step - loss: 0.0017 - mean_absolute_error: 0.0303\n",
            "Epoch 79/100\n",
            "69/69 [==============================] - 45s 650ms/step - loss: 0.0016 - mean_absolute_error: 0.0291\n",
            "Epoch 80/100\n",
            "69/69 [==============================] - 47s 668ms/step - loss: 0.0017 - mean_absolute_error: 0.0302\n",
            "Epoch 81/100\n",
            "69/69 [==============================] - 47s 677ms/step - loss: 0.0017 - mean_absolute_error: 0.0302\n",
            "Epoch 82/100\n",
            "69/69 [==============================] - 46s 671ms/step - loss: 0.0017 - mean_absolute_error: 0.0304\n",
            "Epoch 83/100\n",
            "69/69 [==============================] - 45s 651ms/step - loss: 0.0017 - mean_absolute_error: 0.0307\n",
            "Epoch 84/100\n",
            "69/69 [==============================] - 46s 674ms/step - loss: 0.0017 - mean_absolute_error: 0.0307\n",
            "Epoch 85/100\n",
            "69/69 [==============================] - 46s 673ms/step - loss: 0.0018 - mean_absolute_error: 0.0309\n",
            "Epoch 86/100\n",
            "69/69 [==============================] - 47s 676ms/step - loss: 0.0016 - mean_absolute_error: 0.0296\n",
            "Epoch 87/100\n",
            "69/69 [==============================] - 45s 660ms/step - loss: 0.0015 - mean_absolute_error: 0.0287\n",
            "Epoch 88/100\n",
            "69/69 [==============================] - 46s 671ms/step - loss: 0.0017 - mean_absolute_error: 0.0306\n",
            "Epoch 89/100\n",
            "69/69 [==============================] - 46s 665ms/step - loss: 0.0017 - mean_absolute_error: 0.0297\n",
            "Epoch 90/100\n",
            "69/69 [==============================] - 45s 659ms/step - loss: 0.0017 - mean_absolute_error: 0.0302\n",
            "Epoch 91/100\n",
            "69/69 [==============================] - 49s 702ms/step - loss: 0.0017 - mean_absolute_error: 0.0311\n",
            "Epoch 92/100\n",
            "69/69 [==============================] - 48s 699ms/step - loss: 0.0016 - mean_absolute_error: 0.0296\n",
            "Epoch 93/100\n",
            "69/69 [==============================] - 47s 684ms/step - loss: 0.0017 - mean_absolute_error: 0.0307\n",
            "Epoch 94/100\n",
            "69/69 [==============================] - 48s 690ms/step - loss: 0.0018 - mean_absolute_error: 0.0310\n",
            "Epoch 95/100\n",
            "69/69 [==============================] - 47s 686ms/step - loss: 0.0018 - mean_absolute_error: 0.0308\n",
            "Epoch 96/100\n",
            "69/69 [==============================] - 45s 656ms/step - loss: 0.0017 - mean_absolute_error: 0.0299\n",
            "Epoch 97/100\n",
            "69/69 [==============================] - 47s 678ms/step - loss: 0.0019 - mean_absolute_error: 0.0316\n",
            "Epoch 98/100\n",
            "69/69 [==============================] - 47s 677ms/step - loss: 0.0016 - mean_absolute_error: 0.0298\n",
            "Epoch 99/100\n",
            "69/69 [==============================] - 46s 674ms/step - loss: 0.0019 - mean_absolute_error: 0.0320\n",
            "Epoch 100/100\n",
            "69/69 [==============================] - 46s 663ms/step - loss: 0.0017 - mean_absolute_error: 0.0298\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f8d3088f7f0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "model.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics=[tf.keras.metrics.MeanAbsoluteError()])\n",
        "model.fit(x_train, y_train,epochs = 100)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('keras_model.h5')"
      ],
      "metadata": {
        "id": "zG1W6DhzI-hP"
      },
      "id": "zG1W6DhzI-hP",
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_close.shape"
      ],
      "metadata": {
        "id": "khcKI1sSJSJB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22c3f1bf-201a-4a60-ec6f-3be005f87ab1"
      },
      "id": "khcKI1sSJSJB",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(988, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "past_100_days = pd.DataFrame(train_close[-100:])"
      ],
      "metadata": {
        "id": "6QyF-7KbKaGt"
      },
      "id": "6QyF-7KbKaGt",
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df = pd.DataFrame(test_close)"
      ],
      "metadata": {
        "id": "pKsjLCyGLuOz"
      },
      "id": "pKsjLCyGLuOz",
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Defining the final dataset for testing by including last 100 coloums of the training dataset to get the prediction from the 1st column of the testing dataset.**\n",
        "\n",
        "---\n"
      ],
      "metadata": {
        "id": "d9ZKAzB8oKXJ"
      },
      "id": "d9ZKAzB8oKXJ"
    },
    {
      "cell_type": "code",
      "source": [
        "final_df = past_100_days.append(test_df, ignore_index = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUapqTcVLOj-",
        "outputId": "0b06f215-d627-419d-8e78-461a857ccf4d"
      },
      "id": "UUapqTcVLOj-",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-28-50640d85f7a0>:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  final_df = past_100_days.append(test_df, ignore_index = True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "L5gFYc1hMRjS",
        "outputId": "200e81cd-e7d8-4d77-d727-6d5918b2e074"
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      "id": "L5gFYc1hMRjS",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             0\n",
              "0  2000.000000\n",
              "1  2016.800049\n",
              "2  1982.599976\n",
              "3  1989.750000\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = scaler.fit_transform(final_df)\n",
        "input_data"
      ],
      "metadata": {
        "id": "BU7BPmzvMZpZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "385dad96-22ef-4fe8-8aea-0a5ce1a7f6a6"
      },
      "id": "BU7BPmzvMZpZ",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.15261459],\n",
              "       [0.15966514],\n",
              "       [0.14531224],\n",
              "       ...,\n",
              "       [0.66491104],\n",
              "       [0.66910778],\n",
              "       [0.67745936]])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2E6k84TtMpvD",
        "outputId": "06c0a3bb-bb78-49f3-d233-4ef1922d7a30"
      },
      "id": "2E6k84TtMpvD",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1088, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Testing the model\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "hJzkNc_To9li"
      },
      "id": "hJzkNc_To9li"
    },
    {
      "cell_type": "code",
      "source": [
        "x_test = []\n",
        "y_test = []\n",
        "for i in range(100, input_data.shape[0]):\n",
        "   x_test.append(input_data[i-100: i])\n",
        "   y_test.append(input_data[i, 0])"
      ],
      "metadata": {
        "id": "d9xCof1CMwwh"
      },
      "id": "d9xCof1CMwwh",
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test, y_test = np.array(x_test), np.array(y_test)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6MGDYZvrNUYc",
        "outputId": "4c699113-12cb-4065-b162-29e1761b96a4"
      },
      "id": "6MGDYZvrNUYc",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(988, 100, 1)\n",
            "(988,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Making prediction and plotting the graph of predicted vs actual values\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "PjvHSsrYpFBG"
      },
      "id": "PjvHSsrYpFBG"
    },
    {
      "cell_type": "code",
      "source": [
        "# Making predictions\n",
        "\n",
        "y_pred = model.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GLMExu7NNdmA",
        "outputId": "e415e6ce-3846-4044-e1a3-a1f7b17e10e0"
      },
      "id": "GLMExu7NNdmA",
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "31/31 [==============================] - 2s 56ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wAmVS__yNu-A",
        "outputId": "a7d117ba-f92d-4be6-d813-47147ab5cd1c"
      },
      "id": "wAmVS__yNu-A",
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(988, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HFhTH-1aNyqt",
        "outputId": "0e288060-20c6-46e6-bae7-93bb208f67ec"
      },
      "id": "HFhTH-1aNyqt",
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.20664766, 0.19137155, 0.19265148, 0.19825417, 0.19267248,\n",
              "       0.21302669, 0.19867385, 0.18692296, 0.175298  , 0.17275895,\n",
              "       0.17575959, 0.18356557, 0.19775058, 0.21401296, 0.2351016 ,\n",
              "       0.2543017 , 0.229457  , 0.22232254, 0.2288904 , 0.2497692 ,\n",
              "       0.25870828, 0.26210758, 0.25925388, 0.25942171, 0.25719741,\n",
              "       0.25788994, 0.26168791, 0.26926305, 0.25747026, 0.26823487,\n",
              "       0.2650034 , 0.25929578, 0.25860336, 0.24796458, 0.2531476 ,\n",
              "       0.25841453, 0.25235015, 0.25444853, 0.22106351, 0.22622543,\n",
              "       0.2085782 , 0.19802333, 0.19565221, 0.19777159, 0.21376112,\n",
              "       0.19710007, 0.2018214 , 0.18029209, 0.18490851, 0.19873676,\n",
              "       0.19980695, 0.19323908, 0.20629089, 0.19838009, 0.20717225,\n",
              "       0.2277992 , 0.23894158, 0.22817697, 0.23877375, 0.25694567,\n",
              "       0.24280256, 0.24219405, 0.26093258, 0.25595939, 0.23631861,\n",
              "       0.23839598, 0.22190286, 0.2210215 , 0.23098877, 0.23075793,\n",
              "       0.24326424, 0.2565679 , 0.26857063, 0.25186757, 0.25302167,\n",
              "       0.25058754, 0.26156209, 0.25820469, 0.25560263, 0.24297049,\n",
              "       0.23550027, 0.22935208, 0.21724444, 0.20868312, 0.21218734,\n",
              "       0.21556574, 0.2040876 , 0.21076049, 0.19540037, 0.18008225,\n",
              "       0.15924546, 0.17137401, 0.18973476, 0.1893781 , 0.17617926,\n",
              "       0.19428827, 0.17777404, 0.17789997, 0.18591579, 0.17263303,\n",
              "       0.16105003, 0.15448212, 0.14709586, 0.16142775, 0.16826846,\n",
              "       0.17208749, 0.16560349, 0.17668295, 0.17418581, 0.1820338 ,\n",
              "       0.18702788, 0.20505288, 0.20117089, 0.23438817, 0.25870828,\n",
              "       0.26577973, 0.23692712, 0.2340104 , 0.23732589, 0.2317232 ,\n",
              "       0.23040127, 0.20715125, 0.19498069, 0.22756847, 0.23520652,\n",
              "       0.22582676, 0.21665693, 0.19827518, 0.19817026, 0.20217817,\n",
              "       0.18270522, 0.18681805, 0.1721924 , 0.17540292, 0.18507644,\n",
              "       0.17496224, 0.16144875, 0.17401798, 0.18555902, 0.2035211 ,\n",
              "       0.20448638, 0.17798388, 0.15800739, 0.17019895, 0.16138579,\n",
              "       0.18251638, 0.20580831, 0.22183985, 0.22301496, 0.24874102,\n",
              "       0.24615996, 0.24985311, 0.24309642, 0.2373678 , 0.23591994,\n",
              "       0.22949891, 0.22047591, 0.22295205, 0.21877621, 0.2368222 ,\n",
              "       0.23673829, 0.23900459, 0.25973646, 0.24257182, 0.24223606,\n",
              "       0.23249964, 0.23944517, 0.24783865, 0.25283284, 0.24456528,\n",
              "       0.22410615, 0.2243999 , 0.23944517, 0.23275138, 0.22958282,\n",
              "       0.22364447, 0.22972975, 0.21749628, 0.21046674, 0.18578986,\n",
              "       0.19563121, 0.1978345 , 0.21411788, 0.20647983, 0.20992114,\n",
              "       0.22058083, 0.21699259, 0.22427398, 0.23317106, 0.22991858,\n",
              "       0.2379134 , 0.24315933, 0.23501768, 0.21841954, 0.20102406,\n",
              "       0.21814669, 0.20448638, 0.19689023, 0.15267755, 0.15316018,\n",
              "       0.16780678, 0.18753147, 0.20509488, 0.20148564, 0.14101058,\n",
              "       0.13318364, 0.05602653, 0.07132369, 0.02520146, 0.00908596,\n",
              "       0.00757514, 0.        , 0.06760953, 0.01399613, 0.02803427,\n",
              "       0.04782192, 0.06488164, 0.07896174, 0.05965672, 0.07963321,\n",
              "       0.03038443, 0.00749118, 0.05827177, 0.02899949, 0.05447375,\n",
              "       0.05157799, 0.04146385, 0.03344808, 0.07128168, 0.07650665,\n",
              "       0.04251303, 0.05587965, 0.10151923, 0.0764647 , 0.08403979,\n",
              "       0.09346151, 0.11301832, 0.15867886, 0.12342621, 0.12439149,\n",
              "       0.11215797, 0.10714288, 0.10787731, 0.12546168, 0.13142103,\n",
              "       0.13148399, 0.11163338, 0.10766747, 0.12978429, 0.13106432,\n",
              "       0.13314169, 0.14892143, 0.16115495, 0.12869315, 0.15483888,\n",
              "       0.15441921, 0.14101058, 0.17160485, 0.17240224, 0.17198257,\n",
              "       0.19103579, 0.17286387, 0.18272622, 0.18285214, 0.19825417,\n",
              "       0.18100551, 0.16919172, 0.16516286, 0.17183569, 0.17267504,\n",
              "       0.16860417, 0.17133205, 0.16434446, 0.16742911, 0.17032482,\n",
              "       0.15937133, 0.20249292, 0.19527444, 0.18709079, 0.19124563,\n",
              "       0.21856637, 0.23640253, 0.26307285, 0.26588465, 0.24448126,\n",
              "       0.23837508, 0.24592922, 0.24494294, 0.22477757, 0.25077637,\n",
              "       0.25113314, 0.23686421, 0.23986484, 0.24706232, 0.23275138,\n",
              "       0.22446281, 0.21867129, 0.23940326, 0.28260871, 0.26846571,\n",
              "       0.26991357, 0.27071091, 0.25927478, 0.25740725, 0.26152008,\n",
              "       0.28191629, 0.27656544, 0.27129851, 0.27008139, 0.26061782,\n",
              "       0.2594007 , 0.25423869, 0.25896002, 0.26582174, 0.26030306,\n",
              "       0.25872929, 0.25694567, 0.25673583, 0.25444853, 0.25900203,\n",
              "       0.25686165, 0.25272792, 0.26057581, 0.2560014 , 0.2638912 ,\n",
              "       0.27830705, 0.27381656, 0.2898691 , 0.29874517, 0.29012084,\n",
              "       0.2915897 , 0.3096148 , 0.35884252, 0.35884252, 0.36371078,\n",
              "       0.34606346, 0.34142603, 0.34788908, 0.37208326, 0.34879134,\n",
              "       0.29205139, 0.32984308, 0.33152178, 0.3575835 , 0.35922029,\n",
              "       0.3722931 , 0.44882076, 0.45238799, 0.4614739 , 0.49913967,\n",
              "       0.49378882, 0.50094429, 0.49949644, 0.49238298, 0.46342545,\n",
              "       0.47295203, 0.45123389, 0.46275393, 0.4360626 , 0.43427898,\n",
              "       0.44084695, 0.44143445, 0.41707233, 0.41373594, 0.41812152,\n",
              "       0.43163512, 0.40634974, 0.41852029, 0.42672484, 0.44038526,\n",
              "       0.44951318, 0.45755001, 0.42523508, 0.44126662, 0.43423708,\n",
              "       0.42827772, 0.43394332, 0.4321387 , 0.41560347, 0.41990514,\n",
              "       0.42970457, 0.45656373, 0.45564047, 0.44693223, 0.45503196,\n",
              "       0.43784621, 0.45763392, 0.46718151, 0.45035253, 0.45794868,\n",
              "       0.46220833, 0.48722096, 0.49288657, 0.4817652 , 0.48147144,\n",
              "       0.48738879, 0.4722176 , 0.49431342, 0.5043856 , 0.5139542 ,\n",
              "       0.49681046, 0.51878045, 0.53399365, 0.53424549, 0.54265988,\n",
              "       0.54312156, 0.53422449, 0.51468863, 0.5421773 , 0.58884506,\n",
              "       0.61131865, 0.59390216, 0.58605427, 0.62302752, 0.64634045,\n",
              "       0.64566903, 0.63899615, 0.67750127, 0.67021998, 0.66535172,\n",
              "       0.68169801, 0.70188439, 0.68721679, 0.69949227, 0.69454009,\n",
              "       0.68184494, 0.65477594, 0.61901971, 0.63077059, 0.65767166,\n",
              "       0.6562448 , 0.65137654, 0.63857647, 0.66214125, 0.64652928,\n",
              "       0.66180549, 0.65874185, 0.65236282, 0.63098043, 0.61794952,\n",
              "       0.60296717, 0.5963573 , 0.60244257, 0.55485144, 0.56397936,\n",
              "       0.55050787, 0.57031648, 0.52792937, 0.5404776 , 0.5749539 ,\n",
              "       0.59709173, 0.59314673, 0.57566733, 0.57520565, 0.59367132,\n",
              "       0.60206481, 0.59660904, 0.60005035, 0.61847411, 0.61969113,\n",
              "       0.58760705, 0.59335656, 0.62669967, 0.63213453, 0.62311154,\n",
              "       0.59950485, 0.6003232 , 0.63882832, 0.64692805, 0.6415352 ,\n",
              "       0.67254909, 0.68337671, 0.68618851, 0.70547263, 0.70752899,\n",
              "       0.67575966, 0.61595607, 0.66417661, 0.65418834, 0.64019226,\n",
              "       0.63295288, 0.62214626, 0.61824327, 0.61459213, 0.62768595,\n",
              "       0.62437056, 0.6206564 , 0.58725028, 0.58781688, 0.59316773,\n",
              "       0.61245175, 0.61906162, 0.62806361, 0.63335155, 0.62374105,\n",
              "       0.60905245, 0.59390216, 0.60156122, 0.60955603, 0.60670223,\n",
              "       0.59746939, 0.60607272, 0.60649239, 0.62013181, 0.63880732,\n",
              "       0.64783031, 0.63255421, 0.63908006, 0.63649911, 0.62661576,\n",
              "       0.63156793, 0.63261712, 0.64917325, 0.65628671, 0.65632872,\n",
              "       0.66327435, 0.68719579, 0.68826598, 0.68255837, 0.68742663,\n",
              "       0.70564045, 0.69705814, 0.68690204, 0.69869482, 0.68199176,\n",
              "       0.72907931, 0.73210094, 0.71361427, 0.71533497, 0.71739134,\n",
              "       0.71560772, 0.70887193, 0.7070044 , 0.68236953, 0.68767837,\n",
              "       0.68077475, 0.65964411, 0.65332809, 0.65099888, 0.66233008,\n",
              "       0.65746182, 0.65404151, 0.64976086, 0.65865793, 0.66308552,\n",
              "       0.66161666, 0.65519562, 0.64906833, 0.65530054, 0.65446119,\n",
              "       0.64256338, 0.66436544, 0.69185411, 0.6872587 , 0.69145544,\n",
              "       0.70230406, 0.70771782, 0.71241815, 0.71674082, 0.71990938,\n",
              "       0.76676598, 0.77077389, 0.80438985, 0.80753741, 0.80709673,\n",
              "       0.83978932, 0.82963322, 0.84885433, 0.85424718, 0.87451737,\n",
              "       0.86660657, 0.9023418 , 0.87233508, 0.92345144, 0.92567573,\n",
              "       0.92985147, 0.91470119, 0.89717979, 0.90441917, 0.92706068,\n",
              "       0.94407839, 0.9728891 , 0.95138079, 0.91971638, 0.91789076,\n",
              "       0.93444689, 0.93411113, 0.93709086, 0.93795121, 0.92353535,\n",
              "       0.89927816, 0.904629  , 0.89776739, 0.87873512, 0.89682312,\n",
              "       0.92200358, 0.91260282, 0.94701611, 0.96495719, 0.8600177 ,\n",
              "       0.84627337, 0.84725954, 0.82889879, 0.84388114, 0.83842538,\n",
              "       0.8278077 , 0.79576552, 0.78164352, 0.77916738, 0.77482381,\n",
              "       0.77782444, 0.74924458, 0.7392144 , 0.77222175, 0.77549523,\n",
              "       0.77887363, 0.78575625, 0.78328021, 0.78451823, 0.77553724,\n",
              "       0.77717393, 0.79305864, 0.80449477, 0.8057957 , 0.79131693,\n",
              "       0.771928  , 0.76466761, 0.76712275, 0.75833059, 0.75942168,\n",
              "       0.75982045, 0.78296545, 0.79435957, 0.81477679, 0.84209753,\n",
              "       0.84106935, 0.79740221, 0.81758859, 0.83538274, 0.82482797,\n",
              "       0.83953758, 0.82814336, 0.83408181, 0.81165024, 0.81637157,\n",
              "       0.81752568, 0.80600553, 0.82757686, 0.83699853, 0.8504071 ,\n",
              "       0.85384841, 0.86442428, 0.86880986, 0.86383668, 0.88022498,\n",
              "       0.88215553, 0.91547763, 0.94359581, 0.93360754, 0.91115496,\n",
              "       0.93048099, 0.94153945, 0.95666862, 0.93316686, 0.94911449,\n",
              "       0.97859661, 1.        , 0.98801838, 0.95614403, 0.91917078,\n",
              "       0.9220875 , 0.89600478, 0.89539617, 0.8447625 , 0.86188523,\n",
              "       0.88127417, 0.90830116, 0.93161409, 0.91835244, 0.91428151,\n",
              "       0.89921525, 0.8842958 , 0.89147228, 0.8955851 , 0.8639416 ,\n",
              "       0.88022498, 0.91549863, 0.91352618, 0.90139753, 0.90546835,\n",
              "       0.87420261, 0.81838593, 0.80890135, 0.74085109, 0.79083435,\n",
              "       0.80487243, 0.80149403, 0.8007596 , 0.79234522, 0.77578898,\n",
              "       0.82407253, 0.83777496, 0.8328857 , 0.82373677, 0.84213954,\n",
              "       0.82153348, 0.84755329, 0.85462485, 0.83529882, 0.86645964,\n",
              "       0.87126489, 0.88698178, 0.86918753, 0.86929245, 0.86830627,\n",
              "       0.87930172, 0.88282694, 0.89071684, 0.8955851 , 0.91423961,\n",
              "       0.88928999, 0.85940909, 0.86003861, 0.8645501 , 0.86232591,\n",
              "       0.85009234, 0.79389799, 0.77033321, 0.80596363, 0.83611717,\n",
              "       0.82936047, 0.80235439, 0.80155705, 0.80781015, 0.81750467,\n",
              "       0.80172488, 0.79992026, 0.7736277 , 0.7877497 , 0.75384009,\n",
              "       0.75908602, 0.75642105, 0.74519476, 0.74378881, 0.74641178,\n",
              "       0.73042225, 0.76181391, 0.7606388 , 0.68222261, 0.69525351,\n",
              "       0.70731915, 0.69315514, 0.64264729, 0.667534  , 0.68194986,\n",
              "       0.72977173, 0.72519732, 0.72135724, 0.74981118, 0.75700855,\n",
              "       0.75314757, 0.7245048 , 0.74190028, 0.75153189, 0.72332969,\n",
              "       0.66444946, 0.66065139, 0.65886777, 0.63188269, 0.60959794,\n",
              "       0.61946029, 0.66123889, 0.66585531, 0.70188439, 0.69529552,\n",
              "       0.70436043, 0.70580828, 0.69420433, 0.68438399, 0.70452836,\n",
              "       0.67093341, 0.66306451, 0.68171902, 0.69309223, 0.68369147,\n",
              "       0.62004789, 0.60783533, 0.58855131, 0.57176434, 0.57002273,\n",
              "       0.59893825, 0.60344975, 0.64145128, 0.64669721, 0.64405324,\n",
              "       0.64226963, 0.62092915, 0.65154447, 0.6816141 , 0.69898858,\n",
              "       0.69768765, 0.69474993, 0.71476837, 0.72125232, 0.72549107,\n",
              "       0.72943597, 0.72095857, 0.74960134, 0.74080919, 0.73709503,\n",
              "       0.74062035, 0.73228978, 0.73417831, 0.72108449, 0.69172829,\n",
              "       0.67945282, 0.66386185, 0.66554055, 0.62791679, 0.66090313,\n",
              "       0.62756002, 0.62701443, 0.62827345, 0.62560858, 0.63507225,\n",
              "       0.64348664, 0.66363101, 0.67424879, 0.66854128, 0.62281768,\n",
              "       0.61608199, 0.57594008, 0.58437557, 0.58920183, 0.57279252,\n",
              "       0.57539448, 0.5647558 , 0.56993871, 0.57961223, 0.58725028,\n",
              "       0.57115583, 0.57419847, 0.56597281, 0.61054221, 0.61507471,\n",
              "       0.59952576, 0.62204135, 0.6014773 , 0.61457112, 0.61564131,\n",
              "       0.6138996 , 0.61918754, 0.63301579, 0.62342629, 0.63830373,\n",
              "       0.62995215, 0.64015025, 0.64031818, 0.63834563, 0.64080077,\n",
              "       0.65334899, 0.68127834, 0.6737242 , 0.6590566 , 0.66352609,\n",
              "       0.67036681, 0.66295959, 0.65859492, 0.70488502, 0.71308967,\n",
              "       0.71187266, 0.72142025, 0.71875528, 0.71642607, 0.69126661,\n",
              "       0.70446535, 0.70167455, 0.73182809, 0.735815  , 0.73816522,\n",
              "       0.73904657, 0.73629769, 0.77190699, 0.75658888, 0.75102819,\n",
              "       0.73401048, 0.73201703, 0.71940579, 0.6951486 , 0.69250463,\n",
              "       0.71166282, 0.72561699, 0.70033162, 0.67311569, 0.65708415,\n",
              "       0.67466847, 0.6849925 , 0.68127834, 0.66812161, 0.67842453,\n",
              "       0.68119442, 0.68018725, 0.68507641, 0.68001932, 0.68201277,\n",
              "       0.70295458, 0.70433942, 0.70284966, 0.66107106, 0.70656372,\n",
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          "metadata": {},
          "execution_count": 36
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    {
      "cell_type": "code",
      "source": [
        "y_pred"
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      "metadata": {
        "id": "WDIHSvbHN1vk",
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      "id": "WDIHSvbHN1vk",
      "execution_count": 37,
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              "       [0.6613413 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.scale_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QMFgAgL5N6HX",
        "outputId": "ea7365cc-518f-4580-d114-1804855d66a4"
      },
      "id": "QMFgAgL5N6HX",
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.00041967])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scale_factor = 1/0.00041967\n",
        "y_pred = y_pred * scale_factor\n",
        "y_test = y_test * scale_factor"
      ],
      "metadata": {
        "id": "tPcp9BKLN-Y8"
      },
      "id": "tPcp9BKLN-Y8",
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (12,6))\n",
        "plt.plot(y_test, 'b', label = \"Original Price\")\n",
        "plt.plot(y_pred, 'r', label = \"Predicted Price\")\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Price')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "phumyMZUOPp_",
        "outputId": "eba0f77b-fe47-431e-cbf8-49a103e127ab"
      },
      "id": "phumyMZUOPp_",
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model evaluation"
      ],
      "metadata": {
        "id": "m54ljCJrpShP"
      },
      "id": "m54ljCJrpShP"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculation of mean absolute error"
      ],
      "metadata": {
        "id": "BdnuOyYQRKRP"
      },
      "id": "BdnuOyYQRKRP"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "mae_percentage = (mae / np.mean(y_test)) * 100\n",
        "print(\"Mean absolute error on test set: {:.2f}%\".format(mae_percentage))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yx6JWOKcX3Yo",
        "outputId": "74caad7d-81e9-4866-c771-3e6beccb39d8"
      },
      "id": "Yx6JWOKcX3Yo",
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean absolute error on test set: 6.54%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculation of R2 score"
      ],
      "metadata": {
        "id": "M8XnY8cuQIc2"
      },
      "id": "M8XnY8cuQIc2"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "\n",
        "# Actual values\n",
        "actual = y_test\n",
        "\n",
        "# Predicted values\n",
        "predicted = y_pred\n",
        "\n",
        "# Calculate the R2 score\n",
        "r2 = r2_score(actual, predicted)\n",
        "\n",
        "print(\"R2 score:\", r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GAAyLdoMNTwZ",
        "outputId": "58c4c588-24d4-49bf-83ac-5a615a631c2e"
      },
      "id": "GAAyLdoMNTwZ",
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score: 0.9702856476907452\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the R2 score\n",
        "fig, ax = plt.subplots()\n",
        "ax.barh(0, r2, color='skyblue')\n",
        "ax.set_xlim([-1, 1])\n",
        "ax.set_yticks([])\n",
        "ax.set_xlabel('R2 Score')\n",
        "ax.set_title('R2 Score')\n",
        "\n",
        "# Adding the R2 score value on the bar\n",
        "ax.text(r2, 0, f'{r2:.2f}', va='center', color='black')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "vQGgW50tNtMf",
        "outputId": "94767ec4-b582-409b-ea5d-2d5659997e8c"
      },
      "id": "vQGgW50tNtMf",
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(actual, predicted)\n",
        "plt.plot([min(actual), max(actual)], [min(predicted), max(predicted)], 'r--')\n",
        "plt.xlabel('Actual Values')\n",
        "plt.ylabel('Predicted Values')\n",
        "plt.title(f'R2 Score: {r2:.2f}')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Q8RHQ80hPfi4",
        "outputId": "a1b81f5b-e56b-4f29-886c-e53f7e6f2bcb"
      },
      "id": "Q8RHQ80hPfi4",
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.11"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}